nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical Generative Modeling for Controllable Speech Synthesis

This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions. The model is formulated as a conditional generative model based on the variational autoencoder (VAE) framework, with two levels of hierarchical latent variables. The first level is a categorical variable, which represents attribute groups (e.g. clean/noisy) and provides interpretability. The second level, conditioned on the first, is a multivariate Gaussian variable, which characterizes specific attribute configurations (e.g. noise level, speaking rate) and enables disentangled fine-grained control over these attributes. This amounts to using a Gaussian mixture model (GMM) for the latent distribution. Extensive evaluation demonstrates its ability to control the aforementioned attributes. In particular, we train a high-quality controllable TTS model on real found data, which is capable of inferring speaker and style attributes from a noisy utterance and use it to synthesize clean speech with controllable speaking style.
Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se

Akfract-Several parametric representations of the acoustic signal were  compared  with regard to word  recognition  performance  in  a syllable-oriented continuous speech recognition system. The vocabulary included many phonetically similar monosyllabic words, therefore the  emphasis was on  the  ability to retain  phonetically  significant acoustic information in the face of syntactic and duration variations. For each parameter set (based on a mel-frequency cepstrum, a linear frequency cepstrum, a linear prediction cepstrum, a linear prediction spectrum, or a set of reflection coefficients), word templates were generated using an efficient dynamic warping method, and test data were time registered with the templates. A set of ten mel-frequency cepstrum coefficients computed every 6.4 ms resulted in the best performance, namely 96.5 percent and 95.0 percent recognition with each of two speakers. The superior performance of the mel-frequency cepstrum coefficients may be attributed to the fact that they better represent the perceptually relevant aspects of the short-term speech spectrum.
Reconfigurable Magnetic Resonance-Coupled Wireless Power Transfer System

This paper presents a method for a reconfigurable magnetic resonance-coupled wireless power transfer (R-MRC-WPT) system in order to achieve higher transmission efficiency under various transmission distance and/or misalignment conditions. Higher efficiency, longer transmission distance, and larger misalignment tolerance can be achieved with the presented R-MRC-WPT system when compared to the conventional four-coil MRC-WPT (C-MRC-WPT) system. The reconfigurability in the R-MRC-WPT system is achieved by adaptively switching between different sizes of drive loops and load loops. All drive loops are in the same plane and all load loops are also in the same plane; this method does not require mechanical movements of the drive loop and load loop and does not result in the system volume increase. Theoretical basis of the method for the R-MRC-WPT system is derived based on a circuit model and an analytical model. Results from a proof-of-concept experimental prototype, with transmitter and receiver coil diameter of 60 cm each, show that the transmission efficiency of the R-MRC-WPT system is higher than the transmission efficiency of the C-MRC-WPT system and the capacitor tuning system for all distances up to 200 cm (~3.3 times the coil diameter) and for all lateral misalignment values within 60 cm (one coil diameter).
Eclectic domain mixing for effective adaptation in action spaces

Although videos appear to be very high-dimensional in terms of duration × frame-rate × resolution, temporal smoothness constraints ensure that the intrinsic dimensionality for videos is much lower. In this paper, we use this idea for investigating Domain Adaptation (DA) in videos, an area that remains under-explored. An approach that has worked well for the image DA is based on the subspace modeling of the source and target domains, which works under the assumption that the two domains share a latent subspace where the domain shift can be reduced or eliminated. In this paper, first we extend three subspace based image DA techniques for human action recognition and then combine it with our proposed Eclectic Domain Mixing (EDM) approach to improve the effectiveness of the DA. Further, we use discrepancy measures such as Symmetrized KL Divergence and Target Density Around Source for empirical study of the proposed EDM approach. While, this work mainly focuses on Domain Adaptation in videos, for completeness of the study, we comprehensively evaluate our approach using both object and action datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.
Kinematics performance analysis of a jaw movement robot

In order to reproduce jaw motions and mechanics that match the human jaw function truthfully with the conception of bionics, a novel human jaw movement robot based on mechanical biomimetic principles was proposed. Firstly, based on the biomechanical properties of mandibular muscles, a jaw robot is built based on the 6-PSS parallel mechanism. Secondly, the inverse kinematics solution equations are derived. Finally, kinematics performances, such as workspace with the orientation constant, manipulability, dexterity of the jaw robot are obtained. These indices show that the parallel mechanism have a big enough flexible workspace, no singularity, and a good motion transfer performance for human chewing movement.
Zika Virus Outside Africa

Zika virus (ZIKV) is a flavivirus related to yellow fever, dengue, West Nile, and Japanese encephalitis viruses. In 2007 ZIKV caused an outbreak of relatively mild disease characterized by rash, arthralgia, and conjunctivitis on Yap Island in the southwestern Pacific Ocean. This was the first time that ZIKV was detected outside of Africa and Asia. The history, transmission dynamics, virology, and clinical manifestations of ZIKV disease are discussed, along with the possibility for diagnostic confusion between ZIKV illness and dengue.The emergence of ZIKV outside of its previously known geographic range should prompt awareness of the potential for ZIKV to spread to other Pacific islands and the Americas.
Does the Great Fire Wall cause self-censorship? The effects of perceived internet regulation and the justification of regulation

Does the Great Fire Wall Cause Self-Censorship? The Effects of Perceived Internet Regulation and the Justification of Regulation Zhi-Jin Zhong, Tongchen Wang, Minting Huang, Article information: To cite this document: Zhi-Jin Zhong, Tongchen Wang, Minting Huang, "Does the Great Fire Wall Cause Self-Censorship? The Effects of Perceived Internet Regulation and the Justification of Regulation", Internet Research, https://doi.org/10.1108/IntR-07-2016-0204 Permanent link to this document: https://doi.org/10.1108/IntR-07-2016-0204
Realistic virtual hand modeling with applications for virtual grasping

In virtual environments, virtual hand interactions play key roles in the human-computer interface. Specifically, the virtual grasping of 3D objects provides an intuitive way for users to interact with virtual objects. This paper demonstrates the creation of a sophisticated virtual hand model simulating natural anatomy in its appearance and motion. To achieve good visual realism, the virtual hand is modeled with metaball modeling, and visually enhanced by applying texture mapping. For realistic kinematics modeling, a three-layer model (skeleton, muscle and skin layers) is adopted to handle the motion as well as the deformation of the virtual hand. We also present an approach for virtual grasping of 3D objects with the realistic virtual hand driven by a CyberGlove dataglove. Grasping heuristics are proposed based on the classification with the shapes of objects, and simplified proxies for the virtual hand are used for the purpose of real-time collision detection between the virtual hand and 3D objects.
Research on Social Network Structure and Public Opinions Dissemination of Micro-blog Based on Complex Network Analysis

Micro-blog has become very popular way to transmit and exchange information in network. In order to explore network public opinions dissemination mechanism of Micro-blog, this paper discusses characteristics of the Micro-blog social network structure and influence they made on information transmitting and sharing in Microblog through complex network analysis combined with data obtained from Sina Micro-blog. This paper presents a mechanism building the Micro-blog social network model, and discusses the scale-free property, small-world effect and centrality property of Micro-blog network through complex network analysis. At last, this paper discusses the public opinions dissemination mechanism in Micro-blog social network with a network event case.
Lenient Multi-Agent Deep Reinforcement Learning

Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as stored transitions can become outdated because agents update their policies in parallel [11]. In this work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to decaying temperature values that control the amount of leniency applied towards negative policy updates that are sampled from the ERM. This introduces optimism in the valuefunction update, and has been shown to facilitate cooperation in tabular fully-cooperative multi-agent reinforcement learning problems.We evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN (HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN, that uses average reward learning near terminal states. Evaluations take place in extended variations of the Coordinated Multi-Agent Object Transportation Problem (CMOTP)  which include fully-cooperative sub-tasks and stochastic rewards. We find that LDQN agents are more likely to converge to the optimal policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN agents.
A Wideband Circularly Polarized Conical Beam From a Two-Arm Spiral Antenna Excited in Phase

Analysis is performed for a two-arm round spiral structure excited in phase, where the arms near the antenna center are backed by a small round disc. This spiral antenna has a frequency range where the radiation is bidirectional, having patterns symmetric with respect to the antenna plane. Subsequently, the bidirectional radiation is transformed into a unidirectional conical beam by backing the spiral with a cavity, where the cavity height is chosen to be extremely small (0.077 wavelength at the lowest design frequency 3.3 GHz). It is found that, as the distance between the spiral plane and the small disc inside the cavity is decreased, variations in the axial ratio and VSWR become smaller, as desired. Other antenna characteristics over a wide frequency range of 3.3 GHz to 9.6 GHz, including the gain and radiation efficiency, are also discussed.
Digital preservation in the context of institutional repositories

Institutional repositories are “digital collections that capture and preserve the intellectual output of a single or multi-university community” (Crow, 2002). While some repositories focus on particular subject domains, an institutional repository stores and makes accessible the educational, research and associated assets of an institution. Although most of the currently established institutional repositories are ‘e-prints’ repositories providing open access to the research outputs of a university or research institution, the content does not need to be limited to e-prints but could potentially include research data, learning material, image collections and many other different types of content.
Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources

Transfer learning techniques have been broadly applied in applications where labeled data in a target domain are difficult to obtain while a lot of labeled data are available in related source domains. In practice, there can be multiple source domains that are related to the target domain, and how to combine them is still an open problem. In this paper, we seek to leverage labeled data from multiple source domains to enhance classification performance in a target domain where the target data are received in an online fashion. This problem is known as the online transfer learning problem. To achieve this, we propose novel online transfer learning paradigms in which the source and target domains are leveraged adaptively. We consider two different problem settings: homogeneous transfer learning and heterogeneous transfer learning. The proposed methods work in an online manner, where the weights of the source domains are adjusted dynamically. We provide the mistake bounds of the proposed methods and perform comprehensive experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms.
Creating test environment with UVM for SPI

In order to implement reliable digital system, it is becoming important making tests and finding bugs by setting up a verification environment. It is possible to set up effective verification environment by using Universal Verification Methodology which is standardized and used in worldwide chip industry. In this work, the slave circuit of Serial Peripheral Interface, which is commonly used for communication integrated circuits, have been designed with hardware description and verification language SystemVerilog and creating test environment with UVM.
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation

Variational auto-encoder (VAE) is a powerful unsupervised learning framework for image generation. One drawback of VAE is that it generates blurry images due to its Gaussianity assumption and thus `2 loss. To allow the generation of high quality images by VAE, we increase the capacity of decoder network by employing residual blocks and skip connections, which also enable efficient optimization. To overcome the limitation of `2 loss, we propose to generate images in a multi-stage manner from coarse to fine. In the simplest case, the proposed multi-stage VAE divides the decoder into two components in which the second component generates refined images based on the course images generated by the first component. Since the second component is independent of the VAE model, it can employ other loss functions beyond the `2 loss and different model architectures. The proposed framework can be easily generalized to contain more than two components. Experiment results on the MNIST and CelebA datasets demonstrate that the proposed multi-stage VAE can generate sharper images as compared to those from the original VAE.
Representation and analysis of enterprise models with semantic techniques: an application to ArchiMate, e3value and business model canvas

Enterprise models assist the governance and transformation of organizations through the specification, communication and analysis of strategy, goals, processes, information, along with the underlying application and technological infrastructure. Such models cross-cut different concerns and are often conceptualized using domain-specific modelling languages. This paper explores the application of graph-based semantic techniques to specify, integrate and analyse multiple, heterogeneous enterprise models. In particular, the proposal described in this paper (1) specifies enterprise models as ontological schemas, (2) uses transformation mapping functions to integrate the ontological schemas and (3) analyses the integrated schemas with graph querying and logical inference. The proposal is evaluated through a scenario that integrates three distinct enterprise modelling languages: the business model canvas, e3value, and the business layer of the ArchiMate language. The results show, on the one hand, that the graph-based approach is able to handle the specification, integration and analysis of enterprise models represented with different modelling languages and, on the other, that the integration challenge resides in defining appropriate mapping functions between the schemas.
Fluorine in medicinal chemistry.

Since its first use in the steroid field in the late 1950s, the use of fluorine in medicinal chemistry has become commonplace, with the small electronegative fluorine atom being a key part of the medicinal chemist's repertoire of substitutions used to modulate all aspects of molecular properties including potency, physical chemistry and pharmacokinetics. This review will highlight the special nature of fluorine, drawing from a survey of marketed fluorinated pharmaceuticals and the medicinal chemistry literature, to illustrate key concepts exploited by medicinal chemists in their attempts to optimize drug molecules. Some of the potential pitfalls in the use of fluorine will also be highlighted.
Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting locations, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, a growing line of research has exploited the temporal effect, geographical-social influence, content effect and word-of-mouth effect. However, current research lacks an integrated analysis of the joint effect of the above factors to deal with the issue of data-sparsity, especially in the out-of-town recommendation scenario which has been ignored by most existing work.
 In light of the above, we propose a joint probabilistic generative model to mimic user check-in behaviors in a process of decision making, which strategically integrates the above factors to effectively overcome the data sparsity, especially for out-of-town users. To demonstrate the applicability and flexibility of our model, we investigate how it supports two recommendation scenarios in a unified way, i.e., home-town recommendation and out-of-town recommendation. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets in terms of both recommendation effectiveness and efficiency, and the experimental results show its superiority over other competitors.
Automated Analysis of Secure Internet of Things Protocols

Formal security analysis has proven to be a useful tool for tracking modifications in communication protocols in an automated manner, where full security analysis of revisions requires minimum efforts. In this paper, we formally analysed prominent IoT protocols and uncovered many critical challenges in practical IoT settings. We address these challenges by using formal symbolic modelling of such protocols under various adversaries and security goals. Furthermore, this paper extends formal analysis to cryptographic Denial-of-Service (DoS) attacks and demonstrates that a vast majority of IoT protocols are vulnerable to such resource exhaustion attacks. We present a cryptographic DoS attack countermeasure that can be generally used in many IoT protocols. Our study of prominent IoT protocols such as CoAP and MQTT shows the benefits of our approach.
Signal detection effects on deep neural networks utilizing raw IQ for modulation classification

Recently, automatic modulation classification techniques using convolutional neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making classification decisions directly on the raw IQ samples allows for reduced system complexity and removes the need for expertly crafted transformations and feature extractions. In practice, RF environments are typically very dense, and a receiver must first detect and isolate each signal of interest before classification can be performed. The errors introduced by this detection and isolation process will affect the accuracy of convolutional neural networks making automatic modulation classification decisions using only raw IQ samples. To quantify this impact, a representative convolutional neural network designed to distinguish between 8 modulation classes (2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM, and 64QAM), over a generalized parameter set, is analyzed. The classification accuracy of this neural network is investigated as a function of errors in carrier frequency estimation and errors in sample rate estimation. The importance of defining upper limits on frequency and sample rate estimation errors in a detector is highlighted, and the negative effects of over-estimating or under-estimating these limits is explored.
ON THE OPTIMAL DESIGN OF COLUMNS AGAINST BUCKLING

We establish existence, derive necessary conditions, and construct and test an algorithm for the maximization of a column’s Euler buckling load under a variety of boundary conditions over a general class of admissible designs. We prove that symmetric clamped–clamped columns possess a positive first eigenfunction and introduce a symmetric rearrangement that does not decrease the column’s buckling load. Our necessary conditions, expressed in the language of Clarke’s generalized gradient [10], subsume those proposed by Olhoff and Rasmussen , Masur [22], and Seiranian [34]. The work of , [22], and [34] sought to correct the necessary conditions of Tadjbakhsh and Keller [37] who had not foreseen the presence of a multiple least eigenvalue. This remedy has been hampered by Tadjbakhsh and Keller’s miscalculation of the buckling loads of their clamped-clamped and clamped–hinged columns. We resolve this issue in the appendix. In our numerical treatment of the associated finite dimensional optimization problem we build on the work of Overton [26] in devising an efficient means of extracting an ascent direction from the column’s least eigenvalue. Owing to its possible multiplicity this is indeed a nonsmooth problem and again the ideas of Clarke [10] are exploited.
Workforce management in omnichannel service centers with heterogeneous channel response urgencies

May 2017 Received in revised form 7 September 2017  These decisions are particularly challenging for omnichannel service centers, where customers can request services via different communication channels (e.g., phone, e-mail, live-chat, social media) that have different service quality and response requirements. We present a formulation of the omnichannel workforce management problem that accounts for variations in response urgencies of different channels as well as diminishing agent performances due to channel switching. We develop an algorithm that efficiently provides solutions for this problem and determines the number and channel allocation of service agentswithin the service center. Through numerical experiments, we study the performance of the algorithm among various service center configurations with equal cost characteristics. The results indicate that the proposed algorithm can identify service center structures that outperform many alternative structures, including those commonly-adopted in the real-world.