nils-herrmann/clean_scirepeval huggingface dataset:
CHAPTER 10 Audio-Visual Automatic Speech Recognition : An Overview

We have made significant progress in automatic speech recognition (ASR) for well-defined applications like dictation and medium vocabulary transaction processing tasks in relatively controlled environments. However, ASR performance has yet to reach the level required for speech to become a truly pervasive user interface. Indeed, even in “clean” acoustic environments, and for a variety of tasks, state of the art ASR system performance lags human speech perception by up to an order of magnitude (Lippmann, 1997). In addition, current systems are quite sensitive to channel, environment, and style of speech variations. A number of techniques for improving ASR robustness have met limited success in severely degraded environments, mismatched to system training (Ghitza, 1986; Nadas et al., 1989; Juang, 1991; Liu et al., 1993; Hermansky and Morgan, 1994; Neti, 1994; Gales, 1997; Jiang et al., 2001). Clearly, novel, non-traditional approaches, that use orthogonal sources of information to the acoustic input, are needed to achieve ASR performance closer to the human speech perception level, and robust enough to be deployable in field applications. Visual speech is the most promising source of additional speech information, and it is obviously not affected by the acoustic environment and noise.
Classification and discrimination of focal and non-focal EEG signals based on deep neural network

In this paper, a new model of focal and non-focal electroencephalography classification is carried out using a deep neural network (DNN). The Convolution Architecture For Feature Extraction (Caffe) framework with three different models (LeNet, AlexNet, and GoogLeNet) are applied, where the DNN is trained with different training epoch values (TEs). The performance of discriminating the focal and non-focal EEG signals using soft-max classifier is investigated. This classification serves medical specialists for taking a surgery decision of focal epilepsy patient. In this work, the EEG signals acquired from EEG database in literature works for five epilepsy patients are used for examining the proposed scheme. The results demonstrate a significant performance in terms of the classification accuracy and the remarkable short running time, via few numbers of the training epochs (TEs). However, the first model (LeNet) displays the best performance. Overall, the proposed classification approach provides a better performance as compared with the existing state-of-the-art techniques. Classification accuracy result is 100% for LeNet model at TE=2, while the accuracy of AlexNet reaches to 100% at TE=5, and finally, GoogLeNet touches an accuracy of 100% at TE=10.
Reinforcement Learning as a Framework for Ethical Decision Making

Emerging AI systems will be making more and more decisions that impact the lives of humans in a significant way. It is essential, then, that these AI systems make decisions that take into account the desires, goals, and preferences of other people, while simultaneously learning about what those preferences are. In this work, we argue that the reinforcementlearning framework achieves the appropriate generality required to theorize about an idealized ethical artificial agent, and offers the proper foundations for grounding specific questions about ethical learning and decision making that can promote further scientific investigation. We define an idealized formalism for an ethical learner, and conduct experiments on two toy ethical dilemmas, demonstrating the soundness and flexibility of our approach. Lastly, we identify several critical challenges for future advancement in the area that can leverage our proposed framework.
High performance communication architecture for smart distribution power grid in developing nations

In a smart distribution power grid, cost efficient and reliable communication architecture plays a crucial role in achieving complete functionality. There are different sets of Quality of Services (QoS) requirements for different data packets transmitting inside the microgrid (a regionally limited smart distribution grid), making it challenging to derive optimal communication architecture. The objective of this research work is to determine the optimal communication technologies for each data packet based on its QoS requirement. In this paper, we have proposed an architecture for a smart distribution power grid with Cyber Physical System enabled microgrids, which accommodate almost all functional requirements of a smart distribution power grid. For easy transition towards optimal communication architecture, we have presented a six-tier communication topology, which is derived from the architecture for a smart distribution power grid. The optimization formulations for each packet structure presented in this paper minimize the overall cost and consider the QoS requirements for each packet. Based on the simulation results, we have made recommendations for optimal communication technologies for each packet and thereby developed a heterogeneous communication architecture for
Randomized Controlled Caregiver Mediated Joint Engagement Intervention for Toddlers with Autism

This study aimed to determine if a joint attention intervention would result in greater joint engagement between caregivers and toddlers with autism. The intervention consisted of 24 caregiver-mediated sessions with follow-up 1 year later. Compared to caregivers and toddlers randomized to the waitlist control group the immediate treatment (IT) group made significant improvements in targeted areas of joint engagement. The IT group demonstrated significant improvements with medium to large effect sizes in their responsiveness to joint attention and their diversity of functional play acts after the intervention with maintenance of these skills 1 year post-intervention. These are among the first randomized controlled data to suggest that short-term parent-mediated interventions can have important effects on core impairments in toddlers with autism.
A 20-GHz phase-locked loop for 40-gb/s serializing transmitter in 0.13-/spl mu/m CMOS

A 20-GHz phase-locked loop with 4.9 ps/sub pp//0.65 ps/sub rms/ jitter and -113.5 dBc/Hz phase noise at 10-MHz offset is presented. A half-duty sampled-feedforward loop filter that simply replaces the resistor with a switch and an inverter suppresses the reference spur down to -44.0 dBc. A design iteration procedure is outlined that minimizes the phase noise of a negative-g/sub m/ oscillator with a coupled microstrip resonator. Static frequency dividers made of pulsed latches operate faster than those made of flip-flops and achieve near 2:1 frequency range. The phase-locked loop fabricated in a 0.13-/spl mu/m CMOS operates from 17.6 to 19.4GHz and dissipates 480mW.
BotCop: An Online Botnet Traffic Classifier

A botnet is a network of compromised computers infected with malicious code that can be controlled remotely under a common command and control (C&C) channel. As one the most serious security threats to the Internet, a botnet cannot only be implemented with existing network applications (e.g. IRC, HTTP, or Peer-to-Peer) but also can be constructed by unknown or creative applications, thus making the botnet detection a challenging problem. In this paper, we propose a new online botnet traffic classification system, called BotCop, in which the network traffic are fully classified into different application communities by using payload signatures and a novel decision tree model, and then on each obtained application community, the temporal-frequent characteristic of flows is studied and analyzed to differentiate the malicious communication traffic created by bots from normal traffic generated by human beings. We evaluate our approach with about 30 million flows collected over one day on a large-scale WiFi ISP network and results show that the proposed approach successfully detects an IRC botnet from about 30 million flows with a high detection rate and a low false alarm rate.
Design and Verification of Secure Systems

This paper reviews some of the difficulties that arise in the verification of kernelized secure systems and suggests new techniques for their resolution.
 It is proposed that secure systems should be conceived as distributed systems in which security is achieved partly through the physical separation of its individual components and partly through the mediation of trusted functions performed within some of those components. The purpose of a security kernel is simply to allow such a 'distributed' system to actually run within a single processor; policy enforcement is not the concern of a security kernel.
 This approach decouples verification of components which perform trusted functions from verification of the security kernel. This latter task may be accomplished by a new verification technique called 'proof of separability' which explicitly addresses the security relevant aspects of interrupt handling and other issues ignored by present methods.
High-voltage wordline generator for low-power program operation in NAND flash memories

High-voltage wordline generation circuit with stage-controlled charge pump is developed for low-power NAND flash memories. The proposed stage-controlled charge pump controls an optimized number of stages according to the required program voltage. The proposed high-voltage generator provides sleep-mode operation by turning off the driving clock and regulators during the program verify operation. Using these techniques, the proposed high-voltage generator reduces about 50 percent of power consumption in comparison with a conventional scheme.
Cellular commitment in the developing cerebellum

The mammalian cerebellum is located in the posterior cranial fossa and is critical for motor coordination and non-motor functions including cognitive and emotional processes. The anatomical structure of cerebellum is distinct with a three-layered cortex. During development, neurogenesis and fate decisions of cerebellar primordium cells are orchestrated through tightly controlled molecular events involving multiple genetic pathways. In this review, we will highlight the anatomical structure of human and mouse cerebellum, the cellular composition of developing cerebellum, and the underlying gene expression programs involved in cell fate commitments in the cerebellum. A critical evaluation of the cell death literature suggests that apoptosis occurs in ~5% of cerebellar cells, most shortly after mitosis. Apoptosis and cellular autophagy likely play significant roles in cerebellar development, we provide a comprehensive discussion of their role in cerebellar development and organization. We also address the possible function of unfolded protein response in regulation of cerebellar neurogenesis. We discuss recent advancements in understanding the epigenetic signature of cerebellar compartments and possible connections between DNA methylation, microRNAs and cerebellar neurodegeneration. Finally, we discuss genetic diseases associated with cerebellar dysfunction and their role in the aging cerebellum.
Electric grid state estimators for distribution systems with microgrids

In the development of smart grid, state estimation in distribution systems will likely face more challenges than that at the transmission level. This paper addresses one of such challenges, namely, the absence of topology information, by developing a forecast-aided topology change detection method and an event-triggered recursive Bayesian estimator to identify the correct topology. Simulation studies with microgrid induced changes are presented to illustrate the effectiveness of the proposed algorithm.
Process discovery from event data: Relating models and logs through abstractions

Event data are collected in logistics, manufacturing, finance, healthcare, customer relationship management, e-learning, e-government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, etc. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control-flow model from the event data. This is very challenging, but in recent years many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad-hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an “interface” between both. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block-structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussion on abstractions and process representations is also used to reflect on the gap between process mining literature and commercial process mining tools. This facilitates users to select an appropriate process discovery technique. Moreover, structuring the role of internal abstractions and representations helps to broaden the view and facilitates the creation of new discovery approaches. ∗Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany
New resampling algorithm for particle filter localization for mobile robot with 3 ultrasonic sonar sensor

This paper present a particle filter also known as Monte Carlo Localization (MCL) to solve the localization problem presented before. A new resampling mechanism is proposed. This new resampling mechanism enables the particle filter to converge quicker and more robust to kidnaping problem. This particle filter is simulated in MATLAB and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for MATLAB to connect the robot to MATLAB. The particle filter with the new resampling algorithm can perform very well in the physical experiments.
Spatial Transformer Introspective Neural Network

Natural images contain many variations such as illumination differences, affine transformations, and shape distortions. Correctly classifying these variations poses a long standing problem. The most commonly adopted solution is to build large-scale datasets that contain objects under different variations. However, this approach is not ideal since it is computationally expensive and it is hard to cover all variations in one single dataset. Towards addressing this difficulty, we propose the spatial transformer introspective neural network (ST-INN) that explicitly generates samples with the unseen affine transformation variations in the training set. Experimental results indicate ST-INN achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN and CIFAR-10. We further extend our method to cross dataset classification tasks and few-shot learning problems to verify our method under extreme conditions and observe substantial improvements from experiment results.
Modelling of intelligent intrusion detection system: making a case for snort

Intrusion Detection System (IDS) is a dynamic network security defense technology that can help to provide realtime detection of internal and external attacks on a computer network and alerting the administration for necessary action. However, the inconsistent nature of networks has resulted in a high number of false positives which makes many network administrators thought IDS to be unreliable for today’s network security system. Nowadays, hackers and attackers have created many new viruses and malware to invade one’s computer network system. Hence, this study proposes a method for early detection of an intrusion by using Snort software. The data collected was used to train the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm was simulated using MATLAB software. The performance of this classifier was evaluated based on three parameters: accuracy, sensitivity, and False Positive Rate (FPR). Preprocessing was done to classify the output data into normal and attack. Performance evaluation was done using confusion matrix on the data. The results showed that network-based intrusion detection system could be employed for early detection of intrusion due to the excellent performance recorded which were 94.92% of accuracy, 97.97% for sensitivity, and 0.69% for FPR.
ONTAgri : Scalable Service Oriented Agriculture Ontology for Precision Farming

Although the advancement in sensor technology made inch-by-inch monitoring of agricultural field achievable but precision farming requires monitoring of several context attributes with context modeling to cope up with several distinct problems. Ontology is considered as most promising technique for context modeling. Despite several efforts towards developing agricultural domain ontology, lack of scalable ontology is still a concern. This paper describes scalable service oriented agriculture ontology (ONTAgri) that comprises over several small groups of concepts. The two major groups are system and agriculture. Agriculture group is further divided into core and services (like irrigation, fertilization) concepts. The notation of service oriented ontology made scalability and distribution possible which is the basic need of agriculture domain modeling. Index Terms – Ontology, Context modeling, Precision farming, Sensor,
Conceptual model of enterprise resource planning and business intelligence systems usage

Businesses have invested considerable resources in the usage of enterprise resource planning (ERP) and business intelligence (BI) systems. These systems were heavily studied in developed countries while there are a few and narrowly focused studies in developing ones. However, studies on the integration of ERP and BI have not been given enough attention (hereafter ERPBI). There are many challenges facing the ERPBI usage in term of the steadily increasing speed with which new technologies are evolving. In addition, there are a number of factors that affecting this usage. Based on the finding of the literature, a model from a critical success factors (CSFs) perspective that examine the relationship between ERPBI usage and organisational performance is proposed. The conceptual model provides a foundation for more research in the future. The expected results of the study will improve the business outcome and help design strategies based on an investigation between ERPBI usage and organisational performance.
Bregman Alternating Direction Method of Multipliers

The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance. In this paper, we similarly generalize the alternating direction method of multipliers (ADMM) to Bregman ADMM (BADMM), which allows the choice of different Bregman divergences to exploit the structure of problems. BADMM provides a unified framework for ADMM and its variants, including generalized ADMM, inexact ADMM and Bethe ADMM. We establish the global convergence and the O(1/T ) iteration complexity for BADMM. In some cases, BADMM can be faster than ADMM by a factor of O(n/ lnn) where n is the dimensionality. In solving the linear program of mass transportation problem, BADMM leads to massive parallelism and can easily run on GPU. BADMM is several times faster than highly optimized commercial software Gurobi.
Efficient models for reverberation and distance rendering in computer music and virtual audio reality

This paper discusses efficient digital signal processing algorithms for real-time synthesis of dynamically controllable, natural-sounding artificial reverberation. A general modular framework is proposed for configuring a spatial sound processing and mixing system according to the reproduction format or setup and the listening conditions, over loudspeakers or headphones. In conclusion, the implementation and applications of a spatial sound processing software are described, and approaches to control interface design and effective distance effects are reviewed.
Neuroprotective effect of Bacopa monnieri on beta-amyloid-induced cell death in primary cortical culture.

Bacopa monnieri (Brahmi) is extensively used in traditional Indian medicine as a nerve tonic and thought to improve memory. To examine the neuroprotective effects of Brahmi extract, we tested its protection against the beta-amyloid protein (25-35) and glutamate-induced neurotoxicity in primary cortical cultured neurons.



Neuroprotective effects were determined by measuring neuronal cell viability following beta-amyloid and glutamate treatment with and without Brahmi extract. Mechanisms of neuroprotection were evaluated by monitoring cellular oxidative stress and acetylcholinesterase activity.



Our result demonstrated that Brahmi extract protected neurons from beta-amyloid-induced cell death, but not glutamate-induced excitotoxicity. This neuroprotection was possibly due to its ability to suppress cellular acetylcholinesterase activity but not the inhibition of glutamate-mediated toxicity. In addition, culture medium containing Brahmi extract appeared to promote cell survival compared to neuronal cells growing in regular culture medium. Further study showed that Brahmi-treated neurons expressed lower level of reactive oxygen species suggesting that Brahmi restrained intracellular oxidative stress which in turn prolonged the lifespan of the culture neurons. Brahmi extract also exhibited both reducing and lipid peroxidation inhibitory activities.



From this study, the mode of action of neuroprotective effects of Brahmi appeared to be the results of its antioxidant to suppress neuronal oxidative stress and the acetylcholinesterase inhibitory activities. Therefore, treating patients with Brahmi extract may be an alternative direction for ameliorating neurodegenerative disorders associated with the overwhelming oxidative stress as well as Alzheimer's disease.
Technical evaluation of the Carolo-Cup 2014 - A competition for self-driving miniature cars

The Carolo-Cup competition conducted for the eighth time this year, is an international student competition focusing on autonomous driving scenarios implemented on 1:10 scale car models. Three practical sub-competitions have to be realized in this context and represent a complex, interdisciplinary challenge. Hence, students have to cope with all core topics like mechanical development, electronic design, and programming as addressed usually by robotic applications. In this paper we introduce the competition challenges in detail and evaluate the results of all 13 participating teams from the 2014 competition. For this purpose, we analyze technical as well as non-technical configurations of each student group and derive best practices, lessons learned, and criteria as a precondition for a successful participation. Due to the comprehensive orientation of the Carolo-Cup, this knowledge can be applied on comparable projects and related competitions as well.