nils-herrmann/clean_scirepeval huggingface dataset:
TROPE R HCRAESE R PAID I END-TO-END ACOUSTIC MODELING USING CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATIC SPEECH RECOGNITION

In hidden Markov model (HMM) based automatic speech recognition (ASR) system, modeling the statistical relationship between the acoustic speech signal and the HMM states that represent linguistically motivated subword units such as phonemes is a crucial step. This is typically achieved by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then training a classifier such as artificial neural networks (ANN), Gaussian mixture model that estimates the emission probabilities of the HMM states. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, we propose an end-to-end acoustic modeling approach using convolution neural networks (CNNs), where the CNN takes as input raw speech signal and estimates the HMM states class conditional probabilities at the output. Alternately, in this approach the relevant features and the classifier are jointly learned from the raw speech signal. Through ASR studies and analyses on multiple languages and multiple tasks, we show that: (a) the proposed approach yields consistently a better system with fewer parameters when compared to the conventional approach of cepstral feature extraction followed by ANN training, (b) unlike conventional method of speech processing, in the proposed approach the relevant feature representations are learned by first processing the input raw speech at sub-segmental level (≈ 2 ms). Specifically, through an analysis we show that the filters in the first convolution layer automatically learn ”in-parts” formant-like information present in the sub-segmental speech, and (c) the intermediate feature representations obtained by subsequent filtering of the first convolution layer output are more discriminative compared to standard cepstral features and could be transferred across languages and domains.
Bangladeshi banknote recognition by neural network with axis symmetrical masks

Automated banknote recognition system can be a very good utility in banking systems and other field of commerce. It can also aid visually impaired people. Although in Bangladesh, bill money recognition machines are not common but it is used in other countries. In this paper, for the first time, we have proposed a Neural Network based recognition scheme for Bangladeshi banknotes. The scheme can efficiently be implemented in cheap hardware which may be very useful in many places. The recognition system takes scanned images of banknotes which are scanned by low cost optoelectronic sensors and then fed into a Multilayer Perceptron, trained by Backpropagation algorithm, for recognition. Axis Symmetric Masks are used in preprocessing stage which reduces the network size and guarantees correct recognition even if the note is flipped. Experimental results are presented which show that this scheme can recognize currently available 8 notes (1, 2, 5, 10, 20, 50, 100 & 500 Taka) successfully with an average accuracy of 98.57%.
Cynefin : Uncertainty , Small Worlds and Scenarios

Uncertainty, its modelling and analysis have been discussed across many literatures including statistics and operational research, knowledge management and philosophy. Adherents to Bayesian approaches have usually argued that uncertainty should either be modelled by probabilities or resolved by discussion which clarifies meaning. Others have followed Knight in distinguishing between contexts of risk and of uncertainty: the former admitting modelling and analysis through probability; the latter not. There are also host of approaches in the literatures stemming from Zadeh’s concept of a fuzzy set. Theories of sense-making in the philosophy and management literatures see knowledge and uncertainty as opposite extremes of human understanding and discuss the resolution of uncertainty accordingly. Here we take a Bayesian stance, but a softer one than the conventional, which recognises the concerns in other approaches and, in particular, sets their concerns in the Cynefin framework of decision contexts to reflect on processes of modelling and analysis in statistical, risk and decision analysis. The approach builds on several recent strands of discussion which argue for a convergence of qualitative scenario planning ideas and more quantitative approaches to analysis I discuss how these suggestions and discussions relate to some earlier thinking on the methodology of modelling and, in particular, the concept of a ‘small world’ articulated by Savage.
Technology Insight: noninvasive brain stimulation in neurology—perspectives on the therapeutic potential of rTMS and tDCS

In neurology, as in all branches of medicine, symptoms of disease and the resulting burden of illness and disability are not simply the consequence of the injury, inflammation or dysfunction of a given organ; they also reflect the consequences of the nervous system's attempt to adapt to the insult. This plastic response includes compensatory changes that prove adaptive for the individual, as well as changes that contribute to functional disability and are, therefore, maladaptive. In this context, brain stimulation techniques tailored to modulate individual plastic changes associated with neurological diseases might enhance clinical benefits and minimize adverse effects. In this Review, we discuss the use of two noninvasive brain stimulation techniques—repetitive transcranial magnetic stimulation and transcranial direct current stimulation—to modulate activity in the targeted cortex or in a dysfunctional network, to restore an adaptive equilibrium in a disrupted network for best behavioral outcome, and to suppress plastic changes for functional advantage. We review randomized controlled studies, in focal epilepsy, Parkinson's disease, recovery from stroke, and chronic pain, to illustrate these principles, and we present evidence for the clinical effects of these two techniques.
Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations

The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.
Executive Functions in 5-to 8-Year Olds : Developmental Changes and Relationship to Academic Achievement

Pronounced improvements in executive functions (EF) during preschool years have been documented in cross-sectional studies. However, longitudinal evidence on EF development during the transition to school and predictive associations between early EF and later school achievement are still scarce. This study examined developmental changes in EF across three time-points, the predictive value of EF for mathematical, reading and spelling skills and explored children’s specific academic attainment as a function of early EF. Participants were 323 children following regular education; 160 children were enrolled in prekindergarten (younger cohort: 69 months) and 163 children in kindergarten (older cohort: 78.4 months) at the first assessment. Various tasks of EF were administered three times with an interval of one year each. Mathematical, reading and spelling skills were measured at the last assessment. Individual background characteristics such as vocabulary, non-verbal intelligence and socioeconomic status were included as control variables. In both cohorts, changes in EF were substantial; improvements in EF, however, were larger in preschoolers than school-aged children. EF assessed in preschool accounted for substantial variability in mathematical, reading and spelling achievement two years later, with low EF being especially associated with significant academic disadvantages in early school years. Given that EF continue to develop from preschool into primary school years and that starting with low EF is associated with lower school achievement, EF may be considered as a marker or risk for academic disabilities.
Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing

Datasets which are identical over a number of statistical properties, yet produce dissimilar graphs, are frequently used to illustrate the importance of graphical representations when exploring data. This paper presents a novel method for generating such datasets, along with several examples. Our technique varies from previous approaches in that new datasets are iteratively generated from a seed dataset through random perturbations of individual data points, and can be directed towards a desired outcome through a simulated annealing optimization strategy. Our method has the benefit of being agnostic to the particular statistical properties that are to remain constant between the datasets, and allows for control over the graphical appearance of resulting output.
A Post-failure Analysis of Mobile Payment Platforms

Despite on-going efforts over the past decade, mobile payments are yet to take off successfully. Repeated failures show that mobile payment platforms are complex to launch. The objective of this paper is to unveil factors explaining the failure of past mobile payment platforms. The use of a multi-level framework aims at enriching the variety of issues investigated. In order to explore the reasons of failure, we selected four cases from different countries happening at different times. Our results show that these cases share many of the same failure factors: lack of collaboration between the stakeholders, no technology standard, and low value-added for consumers and merchants compared to existing payment solutions. Even though the list is not exhaustive, the uncovered factors in our study seem to be necessary (but not sufficient) for the further developments of mobile payment platforms.
A decision support system for operations in a container terminal

We describe a variety of inter-related decisions made during daily operations at a container terminal. The ultimate goal of these decisions is to minimize the berthing time of vessels, the resources needed for handling the workload, the waiting time of customer trucks, and the congestion on the roads and at the storage blocks and docks inside the terminal; and to make the best use of the storage space. Given the scale and complexity of these decisions, it is essential to use decision support tools to make them. This paper reports on work to develop such a decision support system (DSS). We discuss the mathematical models and algorithms used in designing the DSS, the reasons for using these approaches, and some experimental results. D 2003 Elsevier B.V. All rights reserved.
A Cocktail Approach for Travel Package Recommendation

Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation.
A 30/35 GHz Dual-Band Transmitter for Phased Arrays in Communication/Radar Applications

A fully integrated single-channel transmitter is proposed for 30/35 GHz dual-band operation for communication/radar phased array systems. Implemented in a 0.25 μm BiCMOS technology, the transmitter achieves 10° phase shift resolution and 5 bit amplitude resolution suitable for advanced beamforming algorithms for suppression and nulling of side lobes. The integration covers from the baseband I/Q modulation to the RF output, including a low phase noise on-chip VCO and dual-band LO generation circuitry. An RF output power of > +14.5 dBm is achieved in both frequency bands by a linear power amplifier. The core circuit occupies a die area of 1.1 mm 2 and consumes 222-241 mA current from a 3.3 V supply.
Topic Models for Image Annotation and Text Illustration

Image annotation, the task of automatically generating description words for a picture, is a key component in various image search and retrieval applications. Creating image databases for model development is, however, costly and time consuming, since the keywords must be hand-coded and the process repeated for new collections. In this work we exploit the vast resource of images and documents available on the web for developing image annotation models without any human involvement. We describe a probabilistic model based on the assumption that images and their co-occurring textual data are generated by mixtures of latent topics. We show that this model outperforms previously proposed approaches when applied to image annotation and the related task of text illustration despite the noisy nature of our dataset.
A Ka-band asymmetrical stacked-FET MMIC Doherty power amplifier

We present a stacked-FET monolithic millimeter-wave (mmW) integrated circuit Doherty power amplifier (DPA). The DPA employs a novel asymmetrical stack gate bias to achieve high power and high efficiency at 6-dB power back-off (PBO). The circuit is fabricated in a 0.15-µm enhancement mode (E-mode) Gallium Arsenide (GaAs) process. Experimental results demonstrate output power at 1-dB gain compression (P1dB) of 28.2 dBm, peak power added efficiency (PAE) of 37% and PAE at 6-dB PBO of 27% at 28 GHz. Measured small signal gain is 15 dB while the 3-dB bandwidth covers from 25.5 to 29.5 GHz. Using digital predistortion (DPD) with a 20 MHz 64 QAM modulated signal, an adjacent channel power ratio (ACPR) of −46 dBc has been observed.
Design of an anthropomorphic prosthetic hand driven by Shape Memory Alloy actuators

This paper presents the mechanical design of an ultra-lightweight, biomimetically actuated anthropomorphic hand for prosthetic purposes. The proposed design is based on an underactuated configuration of 16 joints and 7 active degrees of freedom. Shape Memory Alloy wires are used as motive elements of a specially designed actuation system installed within the envelope of the hand and forearm; due to their inherent contraction ability when heated, these innovative micro-actuators produce linear motion which is imparted to the fingers via a tendon transmission system. The overall design is completed with the integration of the necessary locking mechanisms for power saving. An experimental prototype of the suggested prosthesis has been fabricated using rapid prototyping techniques and it will be used as an evaluation platform for further research towards the development of a truly multifunctional, silent and cosmetically appealing hand for upper limb amputees.
Development of a Social Media Maturity Model -- A Grounded Theory Approach

Social media has become part of everyday lives. Discussions about whether social media is relevant to organizations no longer exist, instead, discussions on how to successfully adopt social media prevail. With much uncertainty on what it takes to reach excellence in social media use, companies often start with an uncoordinated approach, not taking into account all potential threats. However, little research is done on the diverse components of an organization that are influenced by the use of social media. Furthermore, a common understanding on when a company is "ready" for social media application is missing. Thus, this work seeks to describe a generic approach on assessing social media maturity. Applying grounded theory, a first version for a maturity model is developed which will serve as a tool for introducing and assessing organizational social media activities, including a set of relevant demographic information, organizational prerequisites, as well as dimensions of maturity.
Wireless health monitoring system for patients

eHealth is the provision of healthcare with the inclusion of telecommunication techniques. This project looks at the construction of a simple device that will be capable of transferring the data of a patient's vital signs to a remote device wirelessly. The necessity of this project is to alleviate the difficulty that is encountered by medical experts in monitoring multiple patients simultaneously. This project will enable them to observe patients without having to be physically present at their bedside, be it in the hospital or in their home. A patient's body temperature, heart rate and electrocardiography (ECG) are transferred wirelessly through an agent such as Bluetooth technology.
A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard

0957-4174/$ see front matter 2009  Corresponding author. Tel.: +886 33412500x6103 E-mail addresses: hywu@mail.knu.edu.tw, whydec6 ghtzeng@mail.knu.edu.tw (G.-H. Tzeng), j02255@ji-ee The paper proposed a Fuzzy Multiple Criteria Decision Making (FMCDM) approach for banking performance evaluation. Drawing on the four perspectives of a Balanced Scorecard (BSC), this research first summarized the evaluation indexes synthesized from the literature relating to banking performance. Then, for screening these indexes, 23 indexes fit for banking performance evaluation were selected through expert questionnaires. Furthermore, the relative weights of the chosen evaluation indexes were calculated by Fuzzy Analytic Hierarchy Process (FAHP). And the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were respectively adopted to rank the banking performance and improve the gaps with three banks as an empirical example. The analysis results highlight the critical aspects of evaluation criteria as well as the gaps to improve banking performance for achieving aspired/desired level. It shows that the proposed FMCDM evaluation model of banking performance using the BSC framework can be a useful and effective assessment tool.
Testing malware detectors

In today's interconnected world, malware, such as worms and viruses, can cause havoc. A malware detector (commonly known as virus scanner) attempts to identify malware. In spite of the importance of malware detectors, there is a dearth of testing techniques for evaluating them. We present a technique based on program obfuscation for generating tests for malware detectors. Our technique is geared towards evaluating the resilience of malware detectors to various obfuscation transformations commonly used by hackers to disguise malware. We also demonstrate that a hacker can leverage a malware detector's weakness in handling obfuscation transformations and can extract the signature used by a detector for a specific malware. We evaluate three widely-used commercial virus scanners using our techniques and discover that the resilience of these scanners to various obfuscations is very poor.
Integrating Configuration Management with Model-driven Cloud Management based on TOSCA

The paradigm of Cloud computing introduces new approaches to manage IT services going beyond concepts originating in traditional IT service management. The main goal is to automate the whole management of services to reduce costs and to make management tasks less error-prone. Two different service management paradigms are used in practice: configuration management and model-driven Cloud management. The latter one aims to be a holistic management approach for services in the Cloud. However, both management paradigms are originating in different backgrounds, thus model-driven Cloud management does not cover all aspects of configuration management that are key for Cloud services. This paper presents approaches for integrating configuration management with model-driven Cloud management and how they can be realized based on the OASIS Topology and Orchestration Specification for Cloud Applications and Chef, a popular configuration management tool. These approaches enable the creation of holistic and highly portable service models.
Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms

An important issue in anomaly detection in smart manufacturing systems is the lack of consistency in the formal definitions of anomalies, faults, and attacks. The term anomaly is used to cover a wide range of situations that are addressed by different types of solutions. In this letter, we categorize anomalies in machines, controllers, and networks along with their detection mechanisms, and unify them under a common framework to aid in the identification of potential solutions. The main contribution of the proposed categorization is that it allows the identification of gaps in anomaly detection in smart manufacturing systems.
Methods for the prevention, detection and removal of software security vulnerabilities

Over the past decade, the need to build secure software has become a dominant goal in software development. Consequently, software researchers and practitioners have identified ways that malicious users can exploit software and how developers can fix the vulnerabilities. They have also built a variety of source code security checking software applications to partially automate the task of performing a security analysis of a program. Although great advances have been made in this area, the core problem of how the security vulnerabilities occur still exists. An answer to this problem could be a paradigm shift from imperative to functional programming techniques. This may hold the key to removing software vulnerabilities altogether.