nils-herrmann/clean_scirepeval huggingface dataset:
Bidirectional LSTM Networks for Context-Sensitive Keyword Detection in a Cognitive Virtual Agent Framework

Robustly detecting keywords in human speech is an important precondition for cognitive systems, which aim at intelligently interacting with users. Conventional techniques for keyword spotting usually show good performance when evaluated on well articulated read speech. However, modeling natural, spontaneous, and emotionally colored speech is challenging for today’s speech recognition systems and thus requires novel approaches with enhanced robustness. In this article, we propose a new architecture for vocabulary independent keyword detection as needed for cognitive virtual agents such as the SEMAINE system. Our word spotting model is composed of a Dynamic Bayesian Network (DBN) and a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net. The BLSTM network uses a self-learned amount of contextual information to provide a discrete phoneme prediction feature for the DBN, which is able to distinguish between keywords and arbitrary speech. We evaluate our Tandem BLSTM-DBN technique on both read speech and spontaneous emotional speech and show that our method significantly outperforms conventional Hidden Markov Model-based approaches for both application scenarios.
MIMO Space-Time Block Coding ( STBC ) : Simulations and Results

Wireless networks have quickly become part of everyday life. Wireless LANs, cell phone networks, and personal area networks are just a few examples of widely used wireless networks. However, wireless devices are range and data rate limited. The research community has spent a great deal of effort on finding ways to overcome these limitations. One method is to use Multiple-Input Multiple-Output (MIMO) links. The multiple antennas allow MIMO systems to perform precoding (multi-layer beamforming), diversity coding (space-time coding), and spatial multiplexing. Beamforming consists of transmitting the same signal with different gain and phase (called wights) over all transmit antennas such that the receiver signal is maximized. Diversity consists of transmitting a single space-time coded stream through all antennas. Spatial multiplexing increases network capacity by splitting a high rate signal into multiple lower rate streams and transmitting them through the different antennas. In spatial multiplexing, the receiver can successfully decode each stream given that the received signals have sufficient spatial signatures and that the receiver has enough antennas to separate the streams. The result of using these MIMO techniques is higher data rate or longer transmit range without requiring additional bandwidth or transmit power. This paper presents a detailed study of diversity coding for MIMO systems. Different space-time block coding (STBC) schemes including Alamouti’s STBC for 2 transmit antennas as well as orthogonal STBC for 3 and 4 transmit antennas are explored. Finally, these STBC techniques are implemented in MATLAB and analyzed for performance according to their bit-error rates using BPSK, QPSK, 16-QAM, and 64-QAM modulation schemes.
PGMHD: A scalable probabilistic graphical model for massive hierarchical data problems

In the big data era, scalability has become a crucial requirement for any useful computational model. Probabilistic graphical models are very useful for mining and discovering data insights, but they are not scalable enough to be suitable for big data problems. Bayesian Networks particularly demonstrate this limitation when their data is represented using few random variables with a massive set of outcome values for each of them. With hierarchical data - data that is arranged in a treelike structure with several levels - one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian networks become unsuitable for representing the probability distributions for the following reasons: i) each level represents a single random variable with hundreds of thousands of values, ii) the number of levels is usually small, so there are also few random variables, and iii) the structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes. In this paper we propose a scalable probabilistic graphical model to overcome these limitations for massive hierarchical data. We believe the proposed model will lead to an easily-scalable, more readable, and expressive implementation for problems that require probabilistic-based solutions for massive amounts of hierarchical data. We successfully applied this model to solve two different challenging probabilistic-based problems on massive hierarchical data sets for different domains, namely, bioinformatics and latent semantic discovery over search logs.
Expressive Collaborative Music Performance via Machine Learning

To my parents and my girlfriend.  Techniques of Artificial Intelligence and Human-Computer Interaction have empowered computer music systems with the ability to perform with humans via a wide spectrum of applications. However, musical interaction between humans and machines is still far less musical than the interaction between humans since most systems lack any representation or capability of musical expression. This thesis contributes various techniques, especially machine-learning algorithms, to create artificial musicians that perform expressively and collaboratively with humans. The current system focuses on three aspects of expression in human-computer collaborative performance: 1) expressive timing and dynamics, 2) basic improvisation techniques, and 3) facial and body gestures. Timing and dynamics are the two most fundamental aspects of musical expression and also the main focus of this thesis. We model the expression of different musicians as co-evolving time series. Based on this representation, we develop a set of algorithms, including a sophisticated spectral learning method, to discover regularities of expressive musical interaction from rehearsals. Given a learned model, an artificial performer generates its own musical expression by interacting with a human performer given a pre-defined score. The results show that, with a small number of rehearsals, we can successfully apply machine learning to generate more expressive and human-like collaborative performance than the baseline automatic accompaniment algorithm. This is the first application of spectral learning in the field of music. Besides expressive timing and dynamics, we consider some basic improvisation techniques where musicians have the freedom to interpret pitches and rhythms. We developed a model that trains a different set of parameters for each individual measure and focus on the prediction of the number of chords and the number of notes per chord. Given the model prediction, an improvised score is decoded using nearest-neighbor search, which selects the training example whose parameters are closest to the estimation. Our result shows that our model generates more musical, interactive, and natural collaborative improvisation than a reasonable baseline based on mean estimation. Although not conventionally considered to be " music, " body and facial movements are also important aspects of musical expression. We study body and facial expressions using a humanoid saxophonist robot. We contribute the first algorithm to enable a robot to perform an accompaniment for a musician and react to human performance with gestural and facial expression. The current system uses rule-based performance-motion mapping and separates robot motions into three groups: finger motions, …
MuSQLE: Distributed SQL query execution over multiple engine environments

Multi-engine analytics has been gaining an increasing amount of attention from both the academic and the industrial community as it can successfully cope with the heterogeneity and complexity that the plethora of frameworks, technologies and requirements have brought forth. It is now common for a data analyst to combine data that resides on multiple and totally independent engines and perform complex analytics queries. Multi-engine solutions based on SQL can facilitate such efforts, as SQL is a popular standard that the majority of data-scientists understands. Existing solutions propose a middleware that centrally optimizes query execution for multiple engines. Yet, this approach requires manual integration of every primitive engine operator along with its cost model, rendering the process of adding new operators or engines highly inextensible. To address this issue we present MuSQLE, a system for SQL-based analytics over multi-engine environments. MuSQLE can efficiently utilize external SQL engines allowing for both intra and inter engine optimizations. Our framework adopts a novel API-based strategy. Instead of manual integration, MuSQLE specifies a generic API, used for the cost estimation and query execution, that needs to be implemented for each SQL engine endpoint. Our engine API is integrated with a state-of-the-art query optimizer, adding support for location-based, multi-engine query optimization and letting individual runtimes perform sub-query physical optimization. The derived multi-engine plans are executed using the Spark distributed execution framework. Our detailed experimental evaluation, integrating PostgreSQL, MemSQL and SparkSQL under MuSQLE, demonstrates its ability to accurately decide on the most suitable execution engine. MuSQLE can provide speedups of up to 1 order of magnitude for TPCH queries, leveraging different engines for the execution of individual query parts.
A 1.6%/V 124.2 pW 9.3 Hz relaxation oscillator featuring a 49.7 pW voltage and current reference generator

This paper presents a relaxation oscillator that utilizes a supply-stabilized pico-powered voltage and current reference (VCRG) to charge and reset a chopped pair of MIM capacitors at sub-nW power levels. Specifically, a temperature- and line-stabilized reference voltage is generated via a 4-transistor (4T) self-regulated structure, the output of which is used to bias a temperature-compensated gate-leakage transistor to generate a stabilized current reference. The reference current is then used to charge a swapping pair of MIM capacitors to compare to the voltage generated by the same VCRG in a relaxation topology. The design is fabricated in 65 nm CMOS, and 14 measured samples yield a reference voltage of 147.1 mV achieving a temperature coefficient of 364 ppm/°C and a line regulation of 0.21%/V, and a reference current of 10.2 pA achieving a temperature coefficient of 1077.3 ppm/°C and a line regulation of 1.79%/V (all numbers averaged across all samples). The proposed VCRG-based relaxation oscillator achieves an average temperature coefficient of 999.9 ppm/°C from −40 to 120° C and a line regulation of 1.6%/V from 0.6 to 1.1 V, all at a system power consumption of 124.2 pW at 20° C.
Finding your friends and following them to where you are

Location plays an essential role in our lives, bridging our online and offline worlds. This paper explores the interplay between people's location, interactions, and their social ties within a large real-world dataset. We present and evaluate Flap, a system that solves two intimately related tasks: link and location prediction in online social networks. For link prediction, Flap infers social ties by considering patterns in friendship formation, the content of people's messages, and user location. We show that while each component is a weak predictor of friendship alone, combining them results in a strong model, accurately identifying the majority of friendships. For location prediction, Flap implements a scalable probabilistic model of human mobility, where we treat users with known GPS positions as noisy sensors of the location of their friends. We explore supervised and unsupervised learning scenarios, and focus on the efficiency of both learning and inference. We evaluate Flap on a large sample of highly active users from two distinct geographical areas and show that it (1) reconstructs the entire friendship graph with high accuracy even when no edges are given; and (2) infers people's fine-grained location, even when they keep their data private and we can only access the location of their friends. Our models significantly outperform current comparable approaches to either task.
Bayesian Image Super-resolution, Continued

This paper develops a multi-frame image super-resolution approach from a Bayesian view-point by marginalizing over the unknown registration parameters relating the set of input low-resolution views. In Tipping and Bishop’s Bayesian image super-resolution approach , the marginalization was over the superresolution image, necessitating the use of an unfavorable image prior. By integrating over the registration parameters rather than the high-resolution image, our method allows for more realistic prior distributions, and also reduces the dimension of the integral considerably, removing the main computational bottleneck of the other algorithm. In addition to the motion model used by Tipping and Bishop, illumination components are introduced into the generative model, allowing us to handle changes in lighting as well as motion. We show results on real and synthetic datasets to illustrate the efficacy of this approach.
Applying Quantitative Marketing Techniques to the Internet

Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework to implement versioning and price segmentation strategies. Bayesian updating is a natural tool for profiling users with clickstream data. I illustrate these four modeling techniques and discuss their potential for solving Internet marketing problems.
Who plays, how much, and why? Debunking the stereotypical gamer profile

Online games have exploded in popularity, but for many researchers access to players has been difficult. The study reported here is the first to collect a combination of survey and behavioral data with the cooperation of a major virtual world operator. In the current study, 7,000 players of the massively multiplayer online game (MMO) EverQuest 2 were surveyed about their offline characteristics, their motivations and their physical and mental health. These self-report data were then combined with data on participants’ actual in-game play behaviors, as collected by the game operator. Most of the results defy common stereotypes in surprising and interesting ways and have implications for communication theory and for future investigations of games.
Reconstructive Expert System Explanation

Wick, M.R. and W.B. Thompson, Reconstructive expert system explanation, Artificial Intelligence 54 (1992) 33-70. Existing explanation facilities are typically far more appropriate for knowledge engineers engaged in system maintenance than for end-users of the system. This is because the explanation is little more than a trace of the detailed problem-solving steps. An alternative approach recognizes that an effective explanation often needs to substantially reorganize the actual line of reasoning and bring to bear additional information to support the result. Explanation itself becomes a complex problem-solving process that depends not only on the actual line of reasoning, but also on additional knowledge of the domain. This paper presents a new computational model of explanation and argues that it results in significant improvements over traditional approaches.
Hash Visualization : a New Technique to improve Real-World Security

Current security systems su er from the fact that they fail to account for human factors. This paper considers two human limitations: First, people are slow and unreliable when comparing meaningless strings; and second, people have di culties in remembering strong passwords or PINs. We identify two applications where these human factors negatively a ect security: Validation of root keys in public-key infrastructures, and user authentication. Our approach to improve the security of these systems is to use hash visualization, a technique which replaces meaningless strings with structured images. We examine the requirements of such a system and propose the prototypical solution Random Art . We also show how to apply hash visualization to improve the real-world security of root key validation and user authentication.
Burst-mode and ScanSAR interferometry

ScanSAR interferometry is an attractive option for efficient topographic mapping of large areas and for monitoring of large-scale motions. Only ScanSAR interferometry made it possible to map almost the entire landmass of the earth in the 11-day Shuttle Radar Topography Mission. Also the operational satellites RADARSAT and ENVISAT offer ScanSAR imaging modes and thus allow for repeat-pass ScanSAR interferometry. This paper gives a complete description of ScanSAR and burst-mode interferometric signal properties and compares different processing algorithms. The problems addressed are azimuth scanning pattern synchronization, spectral shift filtering in the presence of high squint, Doppler centroid estimation, different phase-preserving ScanSAR processing algorithms, ScanSAR interferogram formation, coregistration, and beam alignment. Interferograms and digital elevation models from RADARSAT ScanSAR Narrow modes are presented. The novel “pack-and-go” algorithm for efficient burst-mode range processing and a new time-variant fast interpolator for interferometric coregistration are introduced.
Algorithm for License Plate Localization and Recognition for Tanzania Car Plate Numbers

In this paper, License plate localization and recognition (LPLR) is presented. It uses image processing and character recognition technology in order to identify the license number plates of the vehicles automatically. This system is considerable interest because of its good application in traffic monitoring systems, surveillance devices and all kind of intelligent transport system. The objective of this work is to design algorithm for License Plate Localization and Recognition (LPLR) of Tanzanian License Plates. The plate numbers used are standard ones with black and yellow or black and white colors. Also, the letters and numbers are placed in the same row (identical vertical levels), resulting in frequent changes in the horizontal intensity. Due to that, the horizontal changes of the intensity have been easily detected, since the rows that contain the number plates are expected to exhibit many sharp variations. Hence, the edge finding method is exploited to find the location of the plate. To increase readability of the plate number, part of the image was enhanced, noise removal and smoothing median filter is used due to easy development. The algorithm described in this paper is implemented using MATLAB 7.11.0(R2010b).
Model Checking and Abstraction

We describe a method for using abstraction to reduce the complexity of temporal logic model checking. The basis of this method is a way of constructing an abstract model of a program without ever examining the corresponding unabstracted model. We show how this abstract model can be used to verify properties of the original program. We have implemented a system based on these techniques, and we demonstrate their practicality using a number of examples, including a pipelined ALU circuit with over 101300 states.
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

Damage to the hippocampal system disrupts recent memory but leaves remote memory intact. The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical synapses change a little on each reinstatement, and that remote memory is based on accumulated neocortical changes. Models that learn via changes to connections help explain this organization. These models discover the structure in ensembles of items if learning of each item is gradual and interleaved with learning about other items. This suggests that the neocortex learns slowly to discover the structure in ensembles of experiences. The hippocampal system permits rapid learning of new items without disrupting this structure, and reinstatement of new memories interleaves them with others to integrate them into structured neocortical memory systems.
Interaction Design - Beyond Human-Computer Interaction, 3rd Edition

A revision of the #1 text in the Human Computer Interaction field, Interaction Design, the third edition is an ideal resource for learning the interdisciplinary skills needed for interaction design, human-computer interaction, information design, web design and ubiquitous computing. The authors are acknowledged leaders and educators in their field, with a strong global reputation. They bring depth of scope to the subject in this new edition, encompassing the latest technologies and devices including social networking, Web 2.0 and mobile devices. The third edition also adds, develops and updates cases, examples and questions to bring the book in line with the latest in Human Computer Interaction. Interaction Design offers a cross-disciplinary, practical and process-oriented approach to Human Computer Interaction, showing not just what principles ought to apply to Interaction Design, but crucially how they can be applied. The book focuses on how to design interactive products that enhance and extend the way people communicate, interact and work. Motivating examples are included to illustrate both technical, but also social and ethical issues, making the book approachable and adaptable for both Computer Science and non-Computer Science users. Interviews with key HCI luminaries are included and provide an insight into current and future trends.
Coherent bag-of audio words model for efficient large-scale video copy detection

Current content-based video copy detection approaches mostly concentrate on the visual cues and neglect the audio information. In this paper, we attempt to tackle the video copy detection task resorting to audio information, which is equivalently important as well as visual information in multimedia processing. Firstly, inspired by bag-of visual words model, a bag-of audio words (BoA) representation is proposed to characterize each audio frame. Different from naive single-based modeling audio retrieval approaches, BoA is a high-level model due to its perceptual and semantical property. Within the BoA model, a coherency vocabulary indexing structure is adopted to achieve more efficient and effective indexing than single vocabulary of standard BoW model. The coherency vocabulary takes advantage of multiple audio features by computing co-occurrence of them across different feature spaces. By enforcing the tight coherency constraint across feature spaces, coherency vocabulary makes the BoA model more discriminative and robust to various audio transforms. 2D Hough transform is then applied to aggregate scores from matched audio segments. The segements fall into the peak bin is identified as the copy segments in reference video. In addition, we also accomplish video copy detection from both audio and visual cues by performing four late fusion strategies to demonstrate complementarity of audio and visual information in video copy detection. Intensive experiments are conducted on the large-scale dataset of TRECVID 2009 and competitve results are achieved.