nils-herrmann/clean_scirepeval huggingface dataset:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs – extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout’s uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout’s uncertainty in deep reinforcement learning.
A reusable BIST with software assisted repair technology for improved memory and IO debug, validation and test time

As silicon integration complexity increases with 3D stacking and Through-Silicon-Via (TSV), so does the occurrence of memory and IO defects and associated test and validation time. This ultimately leads to an overall cost increase. On a 14nm Intel SOC, a reusable BIST engine called Converged-Pattern-Generator-Checker (CPGC) are architected to detect memory and IO defects, and combined with the software assisted repair technology to automatically repair memory cell defects on 3D stacked Wide-IO DRAM. Additionally, we also present the CPGC gate count, power, simulation, and silicon results. The reusable CPGC IP is designed to connect to a standard IP interface, which enables a quick turn-key SOC development cycle. Silicon results show CPGC can speed up validation by 5x, improve test time from minutes down to seconds, and decrease debug time by 5x including root-cause of boot failures of the memory interface. CPGC is also used in memory training and initialization, which makes it a critical part of Intel SOC.
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't

Following up on numerous reports of analogybased identification of “linguistic regularities” in word embeddings, this study applies the widely used vector offset method to 4 types of linguistic relations: inflectional and derivational morphology, and lexicographic and encyclopedic semantics. We present a balanced test set with 99,200 questions in 40 categories, and we systematically examine how accuracy for different categories is affected by window size and dimensionality of the SVD-based word embeddings. We also show that GloVe and SVD yield similar patterns of results for different categories, offering further evidence for conceptual similarity between count-based and neural-net based models.
Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation

Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.
Name disambiguation in author citations using a K-way spectral clustering method

An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies 1. This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles 2. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes.
Open Government Data Implementation Evaluation

This paper analyses the implementation of the Open Government Data strategy and portal of the City of Vienna. This evaluation is based on qualitative interviews and online polls after the strategy was implemented. Two groups of users were involved in the evaluation: internal target groups (employees and heads of department in the City of Vienna’s public administration departments) and external stakeholders (citizens, business representatives, science and research, journalists). Analyzed aspects included the present organizational processes, the benefits (to business and society), and requirements for future Open Government Data initiatives. This evaluation reveals success factors which accompanied the implementation: the clear definition of responsibilities and the implementation along a process model, the integration of the Open Government Data platform into existing Content Management Systems, the evaluation of the Open Government Data initiative very shortly after its inception. Based on the theoretical and empirical findings, recommendations for future Open Government Data strategies are made which target the local authority and would require action on the federal level such as Creative Commons Attribution License as the default for subsidy funds or public relation measures carried out directly by the data providing departments.
Full-Body Compliant Human–Humanoid Interaction: Balancing in the Presence of Unknown External Forces

This paper proposes an effective framework of human-humanoid robot physical interaction. Its key component is a new control technique for full-body balancing in the presence of external forces, which is presented and then validated empirically. We have adopted an integrated system approach to develop humanoid robots. Herein, we describe the importance of replicating human-like capabilities and responses during human-robot interaction in this context. Our balancing controller provides gravity compensation, making the robot passive and thereby facilitating safe physical interactions. The method operates by setting an appropriate ground reaction force and transforming these forces into full-body joint torques. It handles an arbitrary number of force interaction points on the robot. It does not require force measurement at interested contact points. It requires neither inverse kinematics nor inverse dynamics. It can adapt to uneven ground surfaces. It operates as a force control process, and can therefore, accommodate simultaneous control processes using force-, velocity-, or position-based control. Forces are distributed over supporting contact points in an optimal manner. Joint redundancy is resolved by damping injection in the context of passivity. We present various force interaction experiments using our full-sized bipedal humanoid platform, including compliant balance, even when affected by unknown external forces, which demonstrates the effectiveness of the method.
Why does the microbiome affect behaviour?

Growing evidence indicates that the mammalian microbiome can affect behaviour, and several symbionts even produce neurotransmitters. One common explanation for these observations is that symbionts have evolved to manipulate host behaviour for their benefit. Here, we evaluate the manipulation hypothesis by applying evolutionary theory to recent work on the gut–brain axis. Although the theory predicts manipulation by symbionts under certain conditions, these appear rarely satisfied by the genetically diverse communities of the mammalian microbiome. Specifically, any symbiont investing its resources to manipulate host behaviour is expected to be outcompeted within the microbiome by strains that do not manipulate and redirect their resources into growth and survival. Moreover, current data provide no clear evidence for manipulation. Instead, we show how behavioural effects can readily arise as a by-product of natural selection on microorganisms to grow within the host and natural selection on hosts to depend upon their symbionts. We argue that understanding why the microbiome influences behaviour requires a focus on microbial ecology and local effects within the host. The microbiota can influence host behaviour through the gut–brain axis. In this Opinion, Johnson and Foster explore the evolution of this relationship and propose that adaptations of competing gut microorganisms may affect behaviour as a by‑product, leading to host dependence.
Using the Middle to Meddle with Mobile

Researchers and mobile users have little visibility into the network traffic generated by mobile devices and have poor control over how, when, and where that traffic is sent and handled. This paper presents Meddle, a platform that leverages VPNs and software middleboxes to improve transparency and control for Internet traffic from mobile systems. Meddle provides a practical way to interpose on all of a device’s Internet traffic, while providing clear incentives, privacy guarantees, and ease of deployment to end users. We discuss the design and implementation of our system, and evaluate its effectiveness with measurements from an IRB-approved user measurement study. We demonstrate the potential of this platform using case studies of applications built atop Meddle; namely, controlling privacy leaks and detecting ISP interference with Internet traffic.
Review of Design of Speech Recognition and Text Analytics based Digital Banking Customer Interface and Future Directions of Technology Adoption

Banking is one of the most significant adopters of cutting-edge information technologies. Since its modern era beginning in the form of paper based accounting maintained in the branch, adoption of computerized system made it possible to centralize the processing in data centre and improve customer experience by making a more available and efficient system. The latest twist in this evolution is adoption of natural language processing and speech recognition in the user interface between the human and the system and use of machine learning and advanced analytics, in general, for backend processing as well. The paper reviews the progress of technology adoption in the field and comments on the maturity level of solutions involving less studied or lowresource languages like Hindi and also other Indian, regional languages. Furthermore, it also provides an analysis from a prototype built by us. The future directions of this area are also highlighted.
Erasure Coding in Windows Azure Storage

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere, at any time, and only pay for what they use and store. To provide durability for that data and to keep the cost of storage low, WAS uses erasure coding. In this paper we introduce a new set of codes for erasure coding called Local Reconstruction Codes (LRC). LRC reduces the number of erasure coding fragments that need to be read when reconstructing data fragments that are offline, while still keeping the storage overhead low. The important benefits of LRC are that it reduces the bandwidth and I/Os required for repair reads over prior codes, while still allowing a significant reduction in storage overhead. We describe how LRC is used in WAS to provide low overhead durable storage with consistently low read latencies.
Feature Selection as a One-Player Game

This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy. As a second contribution, this paper presents an approximation thereof, based on a one-player game approach and relying on the Monte-Carlo tree search UCT (Upper Confidence Tree) proposed by Kocsis and Szepesvari (2006). The Feature Uct SElection (FUSE) algorithm extends UCT to deal with i) a finite unknown horizon (the target number of relevant features); ii) the huge branching factor of the search tree, reflecting the size of the feature set. Finally, a frugal reward function is proposed as a rough but unbiased estimate of the relevance of a feature subset. A proof of concept of FUSE is shown on benchmark data sets.
On metadata context in Database Forensics

Database Forensics is an important topic that has received hardly any research attention. This paper starts from the premise that this lack of research is due to the inherent complexity of databases that are not fully understood in a forensic context yet. The paper considers the relevant differences between file systems and databases and then transfers concepts of File System Forensics to Database Forensics. It is found that databases are inherently multidimensional from a forensic perspective. A notation is introduced to express the meaning of various possible forensic queries within this multidimensional context. It is posited that this notation, with the multidimensional nature of databases as described, forms a map for possible Database Forensics research projects. a 2008 Elsevier Ltd. All rights reserved.
Mining Sequential Alarm Patterns in a Telecommunication Database

A telecommunication system produces daily a large amount of alarm data which contains hidden valuable information about the system behavior. The knowledge discovered from alarm data can be used in finding problems in networks and possibly in predicting severe faults. In this paper, we devise a solution procedure for mining sequential alarm patterns from the alarm data of a GSM system. First, by observing the features of the alarm data, we develop operations for data cleaning. Then, we transform the alarm data into a set of alarm sequences. Note that the consecutive alarm events exist in the alarm sequences, and it is complicated to count the occurrence counts of events and extract patterns. Hence, we devise a new procedure to determine the occurrence count of the sequential alarm patterns in accordance with the nature of alarms. By utilizing time constraints to restrict the time difference between two alarm events, we devise a mining algorithm to discover useful sequential alarm patterns. The proposed mining algorithm is implemented and applied to test against a set of real alarm data provided by a cellular phone company. The quality of knowledge discovered is evaluated. The experimental results show that the proposed operations of data cleaning are able to improve the execution of our mining algorithm significantly and the knowledge obtained from the alarm data is very useful from the perspective of network operators for alarm prediction and alarm control.
Real-Time Visualization System of Magnetic Field Utilizing Augmented Reality Technology for Education

In electromagnetics education, it is important for beginners, who start to learn electromagnetics, to give an illustration of magnetic field. In this paper we propose a new real-time visualization system. It can visualize a composite image of source materials and their generated magnetic field utilizing the Augmented Reality technique to the users. With this real-time visualization system, electromagnetics learners can observe the visualized magnetic field as a realistic magnetic distribution on real-time and the visualized field changes immediately they move the objects.
Using neural networks and GIS to forecast land use changes : a Land Transformation Model

The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model’s predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan’s Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales.
A Formal Model and Analysis of the MQ Telemetry Transport Protocol

We present a formal model of the MQ Telemetry Transport version 3.1 protocol based on a timed message-passing process algebra. We explain the modeling choices that we made, including pointing out ambiguities in the original protocol specification, and we carry out a static analysis of the formal protocol model, which is based on an approximation of a name-substitution semantics for algebra. The analysis reveals that the protocol behaves correctly as specified against the first two quality of service modes of operation providing at most once and at least once delivery semantics to the subscribers. However, we find that the third and highest quality of service semantics is prone to error and at best ambiguous in certain aspects of its specification. Finally, we suggest an enhancement of this level of QoS for the protocol.
A study to evaluate the safety, tolerability, and efficacy of brodalumab in subjects with rheumatoid arthritis and an inadequate response to methotrexate.

To evaluate the efficacy and safety of brodalumab, a human monoclonal antibody inhibitor of the interleukin 17 receptor, in subjects with rheumatoid arthritis (RA).



Patients (n = 252) with inadequate response to methotrexate (MTX) were randomized to receive subcutaneous injections of brodalumab (70 mg, 140 mg, or 210 mg) or placebo. The primary endpoint was the American College of Rheumatology 50% response (ACR50) at Week 12.



Demographics and baseline characteristics were generally balanced among treatment groups. At Week 12, ACR50 occurred in 16% (70 mg), 16% (140 mg), 10% (210 mg), and 13% (placebo; all nonsignificant vs placebo) of subjects. No significant treatment effects were observed for the secondary endpoints, including ACR20, ACR70, and Disease Activity Score in 28 joints. Incidences of all adverse events (AE), including serious AE (SAE), were similar across treatment groups. A total of 7 subjects reported SAE during the study (2 in the placebo group and 5 in the brodalumab groups), none of which was treatment related. There was 1 death (cardiopulmonary failure) ∼1 week after the last dose in the 140 mg group.



Our study failed to find evidence of meaningful clinical efficacy with brodalumab treatment in subjects with RA who had an inadequate response to MTX. These preliminary results do not support further evaluation of brodalumab as a treatment for RA.
Survey on Sequential Pattern Mining Algorithms

Sequential pattern mining is a significant data-mining method for determining time-related behavior in sequence databases. The information achieved from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. Existing methods only focus on the concept of frequency because of the assumption that sequences&apos; behaviors do not change over time. Several efficient algorithms for maintaining sequential patterns have been developed. , old datasets are deleted while some other datasets are updated. It is obvious time stamp as an important attribute of each dataset, also it is important in the process of data mining and it can gives us more accurate and useful information. Although there have been many recent studies on the sequential patterns in static database. But the complexity of sequential pattern mining is when increasing the data in dynamically, As time passes by new data sets are inserted
Deep learning traffic sign detection, recognition and augmentation

Driving is a complex, continuous, and multitask process that involves driver's cognition, perception, and motor movements. The way road traffic signs and vehicle information is displayed impacts strongly driver's attention with increased mental workload leading to safety concerns. Drivers must keep their eyes on the road, but can always use some assistance in maintaining their awareness and directing their attention to potential emerging hazards. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. In-vehicle contextual Augmented Reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we present a new real-time approach for fast and accurate framework for traffic sign recognition, based on Cascade Deep learning and AR, which superimposes augmented virtual objects onto a real scene under all types of driving situations, including unfavorable weather conditions. Experiments results show that, by combining the Haar Cascade and deep convolutional neural networks show that the joint learning greatly enhances the capability of detection and still retains its realtime performance.
2Vrms 16Ω switching headphone driver with 82% peak efficiency, 102 dB SNDR and 1.1mA/channel quiescent current

A new headphone driver IC based on a switching output stage in continuous conduction mode is described. The driver uses a sliding-mode peak-valley control scheme to regulate the inductor current. The output stage is switched between the battery voltage and the output of an integrated non-regulated inverting charge pump, and a second-order loop filter is used to regulate the output voltage. The system achieves a 2Vrms output audio signal to a 16Ω load while consuming a quiescent average current of 1.1mA/channel and 82% peak efficiency. Peak SNDR is about 102dB, A-weighted DR is 108dB, and the PSRR is more than 120dB.