nils-herrmann/clean_scirepeval huggingface dataset:
Modelling out-of-vocabulary words for robust speech recognition

This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in continuous speech recognition. Most of today's state-of-the-art speech recognition systems can recognize only words that belong to some predefined finite word vocabulary. When encountering an OOV word, a speech recognizer erroneously substitutes the OOV word with a similarly sounding word from its vocabulary. Furthermore, a recognition error due to an OOV word tends to spread errors into neighboring words; dramatically degrading overall recognition performance. In this thesis we propose a novel approach for handling OOV words within a single-stage recognition framework. To achieve this goal, an explicit and detailed model of OOV words is constructed and then used to augment the closed-vocabulary search space of a standard speech recognizer. This OOV model achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words. Examples of such subword units are phones, syllables, or some automatically-learned multi-phone sequences. Subword units have the attractive property of being a closed set, and thus are able to cover any new words, and can conceivably cover most utterances with partially spoken words as well. The main challenge with such an approach is ensuring that the OOV model does not absorb portions of the speech signal corresponding to in-vocabulary (IV) words. In dealing with this challenge, we explore several research issues related to designing the subword lexicon, language model, and topology of the OOV model. We present a dictionary-based approach for estimating subword language models. Such language models are utilized within the subword search space to help recognize the underlying phonetic transcription of OOV words. We also propose a data-driven iterative bottom-up procedure for automatically creating a multi-phone subword inventory. Starting with individual phones, this procedure uses the maximum mutual information principle to successively merge phones to obtain longer subword units. The thesis also extends this OOV approach to modelling multiple classes of OOV words. Instead of augmenting the word search space with a single model, we add several models, one for each class of words. We present two approaches for designing the OOV word classes. The first approach relies on using common part-of-speech tags. The second approach is a data-driven two-step clustering procedure, where the first step uses agglomerative clustering to derive an initial class assignment, while the second step uses iterative clustering to move words from one class to another in order to reduce the model perplexity.
Latent dirichlet allocation based diversified retrieval for e-commerce search

Diversified retrieval is a very important problem on many e-commerce sites, e.g. eBay and Amazon. Using IR approaches without optimizing for diversity results in a clutter of redundant items that belong to the same products. Most existing product taxonomies are often too noisy, with overlapping structures and non-uniform granularity, to be used directly in diversified retrieval. To address this problem, we propose a Latent Dirichlet Allocation (LDA) based diversified retrieval approach that selects diverse items based on the hidden user intents. Our approach first discovers the hidden user intents of a query using the LDA model, and then ranks the user intents by making trade-offs between their relevance and information novelty. Finally, it chooses the most representative item for each user intent to display. To evaluate the diversity in the search results on e-commerce sites, we propose a new metric, average satisfaction, measuring user satisfaction with the search results. Through our empirical study on eBay, we show that the LDA model discovers meaningful user intents and the LDA-based approach provides significantly higher user satisfaction than the eBay production ranker and three other diversified retrieval approaches.
Social Entrepreneurship: A Critique and Future Directions

Work on social entrepreneurship constitutes a field of study that intersects a number of domains, including entrepreneurial studies, social innovation, and nonprofit management. Scholars are beginning to contribute to the development of this new discipline through efforts that attempt to trace the emergence of social entrepreneurship as well as by comparing it to other organizational activities such as conventional entrepreneurship. However, as a nascent field, social entrepreneurship scholars are in the midst of a number of debates involving definitional and conceptual clarity, boundaries of the field, and a struggle to arrive at a set of relevant and meaningful research questions. This paper examines the promise of social entrepreneurship as a domain of inquiry and suggests a number of research areas and research questions for future study.
Sex differences in mate preferences revisited: do people know what they initially desire in a romantic partner?

In paradigms in which participants state their ideal romantic-partner preferences or examine vignettes and photographs, men value physical attractiveness more than women do, and women value earning prospects more than men do. Yet it remains unclear if these preferences remain sex differentiated in predicting desire for real-life potential partners (i.e., individuals whom one has actually met). In the present study, the authors explored this possibility using speed dating and longitudinal follow-up procedures. Replicating previous research, participants exhibited traditional sex differences when stating the importance of physical attractiveness and earning prospects in an ideal partner and ideal speed date. However, data revealed no sex differences in the associations between participants' romantic interest in real-life potential partners (met during and outside of speed dating) and the attractiveness and earning prospects of those partners. Furthermore, participants' ideal preferences, assessed before the speed-dating event, failed to predict what inspired their actual desire at the event. Results are discussed within the context of R. E. Nisbett and T. D. Wilson's (1977) seminal article: Even regarding such a consequential aspect of mental life as romantic-partner preferences, people may lack introspective awareness of what influences their judgments and behavior.
Pattern electrical stimulation of the human retina

Experiments were conducted to study if electrical stimulation of the retinal surface can elicit visual sensation in individuals blind from end-stage retinitis pigmentosa (RP) or age-related macular degeneration (AMD). Under local anesthesia, different stimulating electrodes were inserted through the eyewall and positioned over the surface of the retina. Subjects' psychophysical responses to electrical stimulation were recorded. Subjects perceived simple forms in response to pattern electrical stimulation of the retina. A non-flickering perception was created with stimulating frequencies between 40 and 50 Hz. The stimulation threshold was dependent on the targeted retinal area (macular versus extramacular).
Fundamentals of Wearable Computers and Augmented Reality, Second Edition

The best ebooks about Fundamentals Of Wearable Computers And Augmented Reality Second Edition that you can get for free here by download this Fundamentals Of Wearable Computers And Augmented Reality Second Edition and save to your desktop. This ebooks is under topic such as fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality 11 location-based mixed and augmented reality storytelling fundamentals of wearable computers and augmented reality forum book review mit press journals the vietnam war revised 2nd edition ebook | slangsurfing dicom to 3d holograms: use case for augmented reality in pensees de j j rousseau citoyen de geneve v1 2 1785 ebook click here to access this book : free download amendment xivequal protection constitutional amendments solutions manual to fundamentals of engineering document about distant mirrors america as a foreign regeneration of peasants slangsurfing the cell ebook | esmajorelojes il buddhismo contemporaneo italian edition ebook | slanggeek proceedings of a court of inquiryconvened at the navy answer coming soonmore blog postings on arts letters the travel and tropical medicine manual 4e ebook fundamentals of sql server 2012 replication
Innovative instructions and software model for isolated execution

For years the PC community has struggled to provide secure solutions on open platforms. Intel has developed innovative new technology to enable SW developers to develop and deploy secure applications on open platforms. The technology enables applications to execute with confidentiality and integrity in the native OS environment. It does this by providing ISA extensions for generating hardware enforceable containers at a granularity determined by the developer. These containers while opaque to the operating system are managed by the OS. This paper analyzes the threats and attacks to applications. It then describes the ISA extension for generating a HW based container. Finally it describes the programming model of this container.
ADMM for harmonic retrieval from one-bit sampling with time-varying thresholds

Parameter estimation of sinusoids is a problem of significance in many practical applications. This problem is revisited through a new alternating direction method of multipliers (ADMM) based approach, where the unknown parameters are estimated from one-bit quantized noisy measurements with time varying thresholds. The proposed method is computationally efficient and easy to implement, since each ADMM update has simple closed-form formula. Moreover, it provides accurate estimates by exploiting group sparsity hidden in the signal model. Simulations demonstrate the effectiveness of our algorithm.
Fuzzy Hidden Markov Models for Indonesian Speech Classification

Indonesia has a lot of tribe, so that there are a lot of dialects. Speech classification is difficult if the database uses speech signals from various people who have different characteristics because of gender and dialect. The different characteristics will influence frequency, intonation, amplitude, and period of the speech. It makes the system must be trained for the various templates reference of speech signal. Therefore, this study has been developed for Indonesian speech classification. This study designs the solution of the different characteristics for Indonesian speech classification. The solution combines Fuzzy on Hidden Markov Models. The new design of fuzzy Hidden Markov Models will be proposed in this study. The models will consist of Fuzzy C-Means Clustering which will be designed to substitute the vector quantization process and a new forward and backward method to handle the membership degree of data. The result shows FHMM is better than HMM and the improvement was 3.33 %.
Islamic vs . Conventional Banking : Business Model , Efficiency and Stability

How different are Islamic banks from conventional banks? Does the recent crisis justify a closer look at the Sharia-compliant business model for banking? When comparing conventional and Islamic banks, controlling for time-variant country-fixed effects, we find few significant differences in business orientation. There is evidence however, that Islamic banks are less cost-effective, but have a higher intermediation ratio, higher asset quality and are better capitalized. We also find large cross-country variation in the differences between conventional and Islamic banks as well as across Islamic banks of different sizes. Furthermore, we find that Islamic banks are better capitalized, have higher asset quality and are less likely to disintermediate during crises. The better stock performance of listed Islamic banks during the recent crisis is also due to their higher capitalization and better asset quality.
IEC 61850 impact on substation design

IEC 61850 communication networks and systems for utility automation is a standard for communications that creates an environment that will allow dramatic changes in the design and operation of substations. The paper describes the different components of such systems and the distribution of functions between the devices included in it. Multiple Merging Units communicating over a 100 Mb Ethernet with multiple devices and/or a central computer that receives and processes current and voltage samples with a rate of 256 samples/cycle allow the implementation of all typical substation protection, automation, control, monitoring and recording functions in a very efficient way. The concepts also allow to change the design of layout of the high voltage switchgear.
Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction

Goal recognition is the task of inferring an agent’s goals given some or all of the agent’s observed actions. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-theshell planners. However, obtaining accurate cost or reward models of an agent and incorporating them into the planning model becomes an issue in real applications. Towards this end, we propose an Inverse Reinforcement Learning (IRL)based opponent behavior modeling method, and apply it in the goal recognition assisted Dynamic Local Network Interdiction (DLNI) problem. We first introduce the overall framework and the DLNI problem domain of our work. After that, an IRL-based human behavior modeling method and Markov Decision Process-based goal recognition are introduced. Experimental results indicate that our learned behavior model has a higher tracking accuracy and yields better interdiction outcomes than other models.
IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion

The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively.
Exploiting Geographical Neighborhood Characteristics for Location Recommendation

Geographical characteristics derived from the historical check-in data have been reported effective in improving location recommendation accuracy. However, previous studies mainly exploit geographical characteristics from a user's perspective, via modeling the geographical distribution of each individual user's check-ins. In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location. The neighborhood is modeled at two levels: the instance-level neighborhood defined by a few nearest neighbors of the location, and the region-level neighborhood for the geographical region where the location exists. We propose a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region-level characteristics, i.e., locations in the same geographical region may share similar user preferences. In IRenMF, the two levels of geographical characteristics are naturally incorporated into the learning of latent features of users and locations, so that IRenMF predicts users' preferences on locations more accurately. Extensive experiments on the real data collected from Gowalla, a popular LBSN, demonstrate the effectiveness and advantages of our approach.
Learning to Tell Tales: A Data-driven Approach to Story Generation

Computational story telling has sparked great interest in artificial intelligence, partly because of its relevance to educational and gaming applications. Traditionally, story generators rely on a large repository of background knowledge containing information about the story plot and its characters. This information is detailed and usually hand crafted. In this paper we propose a data-driven approach for generating short children’s stories that does not require extensive manual involvement. We create an end-to-end system that realizes the various components of the generation pipeline stochastically. Our system follows a generate-and-and-rank approach where the space of multiple candidate stories is pruned by considering whether they are plausible, interesting, and coherent.
Throughput Maximization for UAV-Enabled Mobile Relaying Systems

In this paper, we consider a novel mobile relaying technique, where the relay nodes are mounted on unmanned aerial vehicles (UAVs) and hence are capable of moving at high speed. Compared with conventional static relaying, mobile relaying offers a new degree of freedom for performance enhancement via careful relay trajectory design. We study the throughput maximization problem in mobile relaying systems by optimizing the source/relay transmit power along with the relay trajectory, subject to practical mobility constraints (on the UAV's speed and initial/final relay locations), as well as the information-causality constraint at the relay. It is shown that for the fixed relay trajectory, the throughput-optimal source/relay power allocations over time follow a “staircase” water filling structure, with non-increasing and non-decreasing water levels at the source and relay, respectively. On the other hand, with given power allocations, the throughput can be further improved by optimizing the UAV's trajectory via successive convex optimization. An iterative algorithm is thus proposed to optimize the power allocations and relay trajectory alternately. Furthermore, for the special case with free initial and final relay locations, the jointly optimal power allocation and relay trajectory are derived. Numerical results show that by optimizing the trajectory of the relay and power allocations adaptive to its induced channel variation, mobile relaying is able to achieve significant throughput gains over the conventional static relaying.
The raven roosting optimisation algorithm

A significant stream of literature which draws inspiration from the foraging activities of various organisms to design optimisation algorithms has emerged over the past decade. The success of these algorithms across a wide variety of application domains has spurred interest in the examination of the foraging behaviours of other organisms to develop novel and powerful, optimisation algorithms. A variety of animals, including some species of birds and bats, engage in social roosting whereby large numbers of conspecifics gather together to roost, either overnight or for longer periods. It has been claimed that these roosts can serve as information centres to spread knowledge concerning the location of food resources in the environment. In this paper we look at the social roosting and foraging behaviour of one species of bird, the common raven, and take inspiration from this to design a novel optimisation algorithm which we call the raven roosting optimisation algorithm. The utility of the algorithm is assessed on a series of benchmark problems and the results are found to be competitive. We also provide a novel taxonomy which classifies foraging-inspired optimisation algorithms based on the underlying social communication mechanism embedded in the algorithms.
Grassmannian beamforming for multiple-input multiple-output wireless systems

Transmit and receive beamforming is an attractive, low-complexity technique for exploiting the significant diversity that is available in multiple-input and multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector which is difficult to realize in practice. In this paper, we propose a quantized maximum signal-to-noise ratio beamforming technique where the receiver only sends the label of the best weight vector in a predetermined codebook to the transmitter. We develop optimal codebooks for i.i.d. Rayleigh fading matrix channels. We derive the distribution of the optimal transmit weight vector and use this result to bound the SNR degradation as a function of the quantization. A codebook design criterion is proposed by exploiting the connections between the quantization problem and Grassmannian line packing. The design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the maximum distortion with the resulting Grassmannian codebooks follow naturally from the nature of the code. A proof is given that any system using an overcomplete codebook for transmit diversity and maximum ratio combining obtains a diversity on the order of the product of the number of transmit and receive antennas. Bounds on the loss in capacity due to quantization are derived. Monte Carlo simulations are presented that compare the symbol error probability for different quantization strategies.