nils-herrmann/clean_scirepeval huggingface dataset:
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

A method of speaker adaptation for continuous density hidden Markov models (HMMs) is presented. An initial speaker-independent system is adapted to improve the modelling of a new speaker by updating the HMM parameters. Statistics are gathered from the available adaptation data and used to calculate a linear regressionbased transformation for the mean vectors. The transformation matrices are calculated to maximize the likelihood of the adaptation data and can be implemented using the forward–backward algorithm. By tying the transformations among a number of distributions, adaptation can be performed for distributions which are not represented in the training data. An important feature of the method is that arbitrary adaptation data can be used—no special enrolment sentences are needed. Experiments have been performed on the ARPA RM1 database using an HMM system with cross-word triphones and mixture Gaussian output distributions. Results show that adaptation can be performed using as little as 11 s of adaptation data, and that as more data is used the adaptation performance improves. For example, using 40 adaptation utterances, a 37% reduction in error from the speakerindependent system was achieved with supervised adaptation and a 32% reduction in unsupervised mode.
Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)

In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FCN (MFCN) that utilizes two output branches for multi-task learning. One branch is used to learn the surface label, while the other branch is used to learn the edge or boundary of the spliced region. We trained the networks using the CASIA v2.0 dataset, and tested the trained models on the CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform existing splicing localization algorithms, and that the MFCN can achieve finer localization than the SFCN.
A graph model for E-commerce recommender systems

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers’ preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.
Notes on Scalable Blockchain Protocols (version 0.3.2)

We propose a technique for achieving scalable blockchain consensus by means of a “sample-and-fallback” game: split transactions up into collations affecting small portions of the blockchain state, and require that in order for a collation of transactions to be valid, it must be approved by a randomly selected fixed-size sample taken from a large validator pool. In the exceptional case that a bad collation does pass through employ a mechanism by which a node can “challenge” an invalid collation and escalate the decision to a much larger set of validators. Our scheme is designed as a generalized overlay that can be applied to any underlying blockchain consensus algorithm (e.g. proof of work, proof of stake, social-network consensus, M-of-N semi-trusted validators) and almost any state transition function, provided that state changes are sufficiently “localized”. Our basic designs allow for a network with nodes bounded by O(N) computational power to process a transaction load and state size of O(N2− ), though we also propose an experimental “stacking” strategy for achieving arbitrary scalability guarantees up to a maximum of O(exp(N/k)) transactional load.
Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric

Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made.



We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care.



The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to meaningful use of EMR data.
Family transitions following the birth of a sibling: an empirical review of changes in the firstborn's adjustment.

Nearly 80% of children in the United States have at least 1 sibling, indicating that the birth of a baby sibling is a normative ecological transition for most children. Many clinicians and theoreticians believe the transition is stressful, constituting a developmental crisis for most children. Yet, a comprehensive review of the empirical literature on children's adjustment over the transition to siblinghood (TTS) has not been done for several decades. The current review summarizes research examining change in first borns' adjustment to determine whether there is evidence that the TTS is disruptive for most children. Thirty studies addressing the TTS were found, and of those studies, the evidence did not support a crisis model of developmental transitions, nor was there overwhelming evidence of consistent changes in firstborn adjustment. Although there were decreases in children's affection and responsiveness toward mothers, the results were more equivocal for many other behaviors (e.g., sleep problems, anxiety, aggression, regression). An inspection of the scientific literature indicated there are large individual differences in children's adjustment and that the TTS can be a time of disruption, an occasion for developmental advances, or a period of quiescence with no noticeable changes. The TTS may be a developmental turning point for some children that portends future psychopathology or growth depending on the transactions between children and the changes in the ecological context over time. A developmental ecological systems framework guided the discussion of how child, parent, and contextual factors may contribute to the prediction of firstborn children's successful adaptation to the birth of a sibling.
Impact of robot failures and feedback on real-time trust

Prior work in human trust of autonomous robots suggests the timing of reliability drops impact trust and control allocation strategies. However, trust is traditionally measured post-run, thereby masking the real-time changes in trust, reducing sensitivity to factors like inertia, and subjecting the measure to biases like the primacy-recency effect. Likewise, little is known on how feedback of robot confidence interacts in real-time with trust and control allocation strategies. An experiment to examine these issues showed trust loss due to early reliability drops is masked in traditional post-run measures, trust demonstrates inertia, and feedback alters allocation strategies independent of trust. The implications of specific findings on development of trust models and robot design are also discussed.
Microcontrollers as material: crafting circuits with paper, conductive ink, electronic components, and an "untoolkit"

Embedded programming is typically made accessible through modular electronics toolkits. In this paper, we explore an alternative approach, combining microcontrollers with craft materials and processes as a means of bringing new groups of people and skills to technology production. We have developed simple and robust techniques for drawing circuits with conductive ink on paper, enabling off-the-shelf electronic components to be embedded directly into interactive artifacts. We have also developed an set of hardware and software tools -- an instance of what we call an "untoolkit" -- to provide an accessible toolchain for the programming of microcontrollers. We evaluated our techniques in a number of workshops, one of which is detailed in the paper. Four broader themes emerge: accessibility and appeal, the integration of craft and technology, microcontrollers vs. electronic toolkits, and the relationship between programming and physical artifacts. We also expand more generally on the idea of an untoolkit, offering a definition and some design principles, as well as suggest potential areas of future research.
A novel delay-aware routing algorithm (DARA) for a hybrid wireless-optical broadband access network (WOBAN)

A hybrid wireless-optical broadband access network (WOBAN) is a promising architecture for future access networks. Recently, the WOBAN has been gaining increasing attention, and early versions are being deployed as municipal access solutions. This architecture saves on network deployment cost because fiber need not penetrate to each end user. However, a major research opportunity exists in developing an efficient routing algorithm for the wireless front-end of the WOBAN. We propose and investigate the characteristics of the delay-aware routing algorithm (DARA) that minimizes the average packet delay in the wireless front-end of a WOBAN. In DARA we model wireless routers as queues and predict wireless link states periodically. Our performance studies show that DARA achieves less delay and congestion, and improved load balancing compared to traditional approaches such as the minimum-hop routing algorithm, shortest-path routing algorithm, and predictive throughput routing algorithm.
Dynamic CPU Resource Allocation in Containerized Cloud Environments

In recent years, lighter-weight virtualization solutions have begun to emerge as an alternative to virtual machines. Because these solutions are still in their infancy, however, several research questions remain open in terms of how to effectively manage computing resources. One important problem is the management of resources in the event of overutilization. For some applications, overutilization can severely affect performance. We provide a solution to this problem by extending the concept of timeslicing to the level of virtualization container. Through this approach we can control and mitigate some of the more detrimental performance effects oversubscription. Our results show significant improvement over standard scheduling with Docker.
The Graph Traversal Pattern

A graph is a structure composed of a set of vertices (i.e. nodes, dots) connected to one another by a set of edges (i.e. links, lines). The concept of a graph has been around since the late 19 century, however, only in recent decades has there been a strong resurgence in both theoretical and applied graph research in mathematics, physics, and computer science. In applied computing, since the late 1960s, the interlinked table structure of the relational database has been the predominant information storage and retrieval model. With the growth of graph/network-based data and the need to efficiently process such data, new data management systems have been developed. In contrast to the index-intensive, set-theoretic operations of relational databases, graph databases make use of index-free, local traversals. This article discusses the graph traversal pattern and its use in computing.
Team Effectiveness 1997-2007 : A Review of Recent Advancements and a Glimpse Into the Future

The authors review team research that has been conducted over the past 10 years. They discuss the nature of work teams in context and note the substantive differences underlying different types of teams. They then review representative studies that have appeared in the past decade in the context of an enhanced input-process-outcome framework that has evolved into an inputsmediators-outcome time-sensitive approach. They note what has been learned along the way and identify fruitful directions for future research. They close with a reconsideration of the typical team research investigation and call for scholars to embrace the complexity that surrounds modern team-based organizational designs as we move forward.
Multi-Language Multi-Speaker Acoustic Modeling for LSTM-RNN Based Statistical Parametric Speech Synthesis

Building text-to-speech (TTS) systems requires large amounts of high quality speech recordings and annotations, which is a challenge to collect especially considering the variation in spoken languages around the world. Acoustic modeling techniques that could utilize inhomogeneous data are hence important as they allow us to pool more data for training. This paper presents a long short-term memory (LSTM) recurrent neural network (RNN) based statistical parametric speech synthesis system that uses data from multiple languages and speakers. It models language variation through cluster adaptive training and speaker variation with speaker dependent output layers. Experimental results have shown that the proposed multilingual TTS system can synthesize speech in multiple languages from a single model while maintaining naturalness. Furthermore, it can be adapted to new languages with only a small amount of data.
Weakly Supervised Semantic Segmentation Based on Co-segmentation

Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of pixel-level masks, which involves a large amount of human labour and time for annotation. In contrast, image-level labels are much easier to obtain. In this work, we propose a novel method for weakly supervised semantic segmentation with only image-level labels. The method relies on a large scale co-segmentation framework that can produce object masks for a group of images containing objects belonging to the same semantic class. We first retrieve images from search engines, e.g. Flickr and Google, using semantic class names as queries, e.g. class names in PASCAL VOC 2012. We then use high quality masks produced by co-segmentation on the retrieved images as well as the target dataset images with image level labels to train segmentation networks. We obtain IoU 56.9 on test set of PASCAL VOC 2012, which reaches state of the art performance.
Program synthesis by sketching

Program Synthesis by Sketching by Armando Solar-Lezama Doctor in Philosophy in Engineering-Electrical Engineering and Computer Science University of California, Berkeley Rastislav Bodik, Chair The goal of software synthesis is to generate programs automatically from highlevel speci cations. However, e cient implementations for challenging programs require a combination of high-level algorithmic insights and low-level implementation details. Deriving the low-level details is a natural job for a computer, but the synthesizer can not replace the human insight. Therefore, one of the central challenges for software synthesis is to establish a synergy between the programmer and the synthesizer, exploiting the programmer's expertise to reduce the burden on the synthesizer. This thesis introduces sketching, a new style of synthesis that o ers a fresh approach to the synergy problem. Previous approaches have relied on meta-programming, or variations of interactive theorem proving to help the synthesizer deduce an e cient implementation. The resulting systems are very powerful, but they require the programmer to master new formalisms far removed from traditional programming models. To make synthesis accessible, programmers must be able to provide their insight e ortlessly, using formalisms they already understand. In Sketching, insight is communicated through a partial program, a sketch that expresses the high-level structure of an implementation but leaves holes in place of the lowlevel details. This form of synthesis is made possible by a new SAT-based inductive synthesis procedure that can e ciently synthesize an implementation from a small number of test cases. This algorithm forms the core of a new counterexample guided inductive synthesis procedure (CEGIS) which combines the inductive synthesizer with a validation procedure to automatically generate test inputs and ensure that the generated program satis es its
Describing Clothing by Semantic Attributes

Describing clothing appearance with semantic attributes is an appealing technique for many important applications. In this paper, we propose a fully automated system that is capable of generating a list of nameable attributes for clothes on human body in unconstrained images. We extract low-level features in a pose-adaptive manner, and combine complementary features for learning attribute classifiers. Mutual dependencies between the attributes are then explored by a Conditional Random Field to further improve the predictions from independent classifiers. We validate the performance of our system on a challenging clothing attribute dataset, and introduce a novel application of dressing style analysis that utilizes the semantic attributes produced by our system.
Progress & Compress: A scalable framework for continual learning

We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent problems. This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task. After learning a new task, the active column is distilled into the knowledge base, taking care to protect any previously acquired skills. This cycle of active learning (progression) followed by consolidation (compression) requires no architecture growth, no access to or storing of previous data or tasks, and no task-specific parameters. We demonstrate the progress & compress approach on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation.
Design of Two-Stage CMOS Operational Amplifier

This paper presents a design of Two Stage CMOS operational amplifier, which operates at ±2.5V power supply using umc 2μm CMOS technology. The OP-AMP designed is a two-stage CMOS OP-AMP. The OP-AMP is designed to exhibit a unity gain frequency of 4.416MHz and exhibits a gain of 96dB with a 700 phase margin. Design and Simulation has been carried out in LT Spice tools.  2 stage CMOS op-amp, design, simulation and results.
ACPN: A Novel Authentication Framework with Conditional Privacy-Preservation and Non-Repudiation for VANETs

In Vehicular Ad hoc NETworks (VANETs), authentication is a crucial security service for both inter-vehicle and vehicle-roadside communications. On the other hand, vehicles have to be protected from the misuse of their private data and the attacks on their privacy, as well as to be capable of being investigated for accidents or liabilities from non-repudiation. In this paper, we investigate the authentication issues with privacy preservation and non-repudiation in VANETs. We propose a novel framework with preservation and repudiation (ACPN) for VANETs. In ACPN, we introduce the public-key cryptography (PKC) to the pseudonym generation, which ensures legitimate third parties to achieve the non-repudiation of vehicles by obtaining vehicles' real IDs. The self-generated PKCbased pseudonyms are also used as identifiers instead of vehicle IDs for the privacy-preserving authentication, while the update of the pseudonyms depends on vehicular demands. The existing ID-based signature (IBS) scheme and the ID-based online/offline signature (IBOOS) scheme are used, for the authentication between the road side units (RSUs) and vehicles, and the authentication among vehicles, respectively. Authentication, privacy preservation, non-repudiation and other objectives of ACPN have been analyzed for VANETs. Typical performance evaluation has been conducted using efficient IBS and IBOOS schemes. We show that the proposed ACPN is feasible and adequate to be used efficiently in the VANET environment.
Lightweight Internet protocols for web enablement of sensors using constrained gateway devices

Lightweight Internet protocols are increasingly being used in ubiquitous environment in order to optimize the resource usage of a constrained device like a smart mobile gateway. This paper presents a study on the various such protocols to optimize the usage of energy, and network resources, computation cost of a constrained gateway device. Comprehensive analysis and feature wise categorization of existing dominant protocols, namely MQTT (message queue telemetry transport), CoAP (constrained application protocol) are provided here to achieve improved understanding of the existing issues and gaps in this domain. The present work further identifies the best suited application areas for each protocol based on the results corresponding to typical resource requirements and performance attributes. Finally our vision on the research scope is presented here mainly for optimization of energy usage.