nils-herrmann/clean_scirepeval huggingface dataset:
Power Optimized Voltage Level Shifter Design for High Speed Dual Supply

1Mtech scholar, Department of ECE, GECI, Kerala, India 2Assistant Professor, Department of ECE, GECI, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract – Reduction of power consumption has always been major design goal in energy harvesting digital devices. One effective way for low power design use of multiple voltages depends on their speed. This, however, accomplish the use of interface block called level converters or level shifters. This paper presents a modified structure of level shifter for lower power implementation. Simulation results of proposed structure in a 0.18-um CMOS technology show that the design of input low supply voltage of 0.4V and high supply voltage of 1.8V, the level shifter has a propagation delay of 12.5ns and a power dissipation of 87.7nW for a 1-MHz input signal. Then comparison is done against these level shifters in terms of power, speed and power delay product.
A Mobile Decision Support System for Dynamic Group Decision-Making Problems

The aim of this paper is to present a decision support system model with two important characteristic: 1) mobile technologies are applied in the decision process and 2) the set of alternatives is not fixed over time to address dynamic decision situations in which the set of solution alternatives could change throughout the decision-making process. We implement a prototype of such mobile decision support system in which experts use mobile phones to provide their preferences anywhere and anytime. To get a general system, experts' preferences are assumed to be represented by different preference representations: 1) fuzzy preference relations; 2) orderings; 3) utility functions; and 4) multiplicative preference relations. Because this prototype incorporates both selection and consensus processes, it allows us to model group decision-making situations. The prototype incorporates a tool for managing the changes on the set of feasible alternatives that could happen throughout the decision process. This way, the prototype provides a new approach to deal with dynamic group decision-making situations to help make decisions anywhere and anytime.
Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques

Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.
Awareness and Coordination in Shared Work Spaces

8 Tackling the first question, we can certainly imagine asynchronous awareness information presented in the same workspace as the work object. A primitive example of this type of asynchronous awareness information is the use of " change bars " ; margin marks indicating text areas which have been changed. Change bars are examples of document-based representations of activity. There is no reason why change bars or similar representations 1 cannot provide further information, such as the nature of changes, the identity of the collaborator making the change, and so forth. Details of past activity can be held within the document, and retain the advantages of passive collection and distribution. What's more, such document-based representations can be presented at various levels of specificity, so that users can access information as it becomes relevant. Such information can also change over time to reflect the progress of activity on the part of individuals; and it can be presented differently to the various collaborators, as is appropriate for their different involvements in the activity and for the way these involvements change over time. The work space, then, holds more than merely the object of group activity, but also becomes a persistent record of that activity. When we consider presenting past activity information within the shared workspace, the division between synchronous and asynchronous activities becomes less distinct, and we can take this further. A semi-synchronous system supports both synchronous and asynchronous work modes. In asynchronous use, the workspace presents past activity information, so as to give an individual awareness of the activities of other participants integrated with the work object itself. In synchronous use, this information is presented as it happens, providing participants with awareness of others' current activities. However, these are not two different modes of the system, but rather are two facets of a single view of awareness information. A semi-synchronous system presents current information on synchronously co-present collaborators, at the same time as representations of past activities by other collaborators who are not synchronously present. The workspace becomes a persistent space in which collaborators can interact, rather like a room in which one can either talk with other people who happen to be there at the time, or leave notes (or other, more passive, representations of one's activity) for those who arrive later. 6  We have discussed approaches to the critical issue of group activity awareness in collaborative systems. This awareness …
Digital movie piracy: A perspective on downloading behavior through social cognitive theory

The current article attempts to refine and specify a model based on other researchers' applications of the Social Cognitive Theory originally posited by Bandura to allow the behaviour and attitudes downloaders exhibit to be analysed and compared to the amount of movies they consume (measured as an monthly average estimate). The new model is then tested against data obtained from college students as well as from a technological lifestyle forum. After the revisions these analyses suggested were applied, the model explained nearly 23% of the variance in the number of movies downloaded. The most important factors in this model were, among others: the drives to see many different and new movies, the social environment and its perceived attitude toward the behaviour, and the degree to which downloading has embedded itself in the daily routine. Because the Dutch government and lobbyists are superficially unclear about the current legislature, the unique opportunity existed to study the impact of knowing about legislature on the behaviour. Other results indicate an unexpected openness to an alternative film-distribution method where both the producers and the consumers get an honest deal, presenting options to release the current political and social tension without prosecuting an increasing portion of the population.
Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval

Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large-scale cross-view retrieval tasks. Most cross-view hashing methods are developed by assuming that data from different views are well paired, e.g., text-image pairs. In real-world applications, however, this fully-paired multiview setting may not be practical. The more practical yet challenging semi-paired cross-view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi-paired cross-view retrieval, dubbed semi-paired discrete hashing (SPDH). In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi-paired data in the latent subspace, we construct the cross-view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization-based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit-by-bit manner with each bit generated with a closed-form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully-paired and semi-paired settings and the results demonstrate the superiority of SPDH over several other state-of-the-art methods in term of both accuracy and scalability.
Super Mario as a String: Platformer Level Generation Via LSTMs

The procedural generation of video game levels has existed for at least 30 years, but only recently have machine learning approaches been used to generate levels without specifying the rules for generation. A number of these have looked at platformer levels as a sequence of characters and performed generation using Markov chains. In this paper we examine the use of Long ShortTerm Memory recurrent neural networks (LSTMs) for the purpose of generating levels trained from a corpus of Super Mario Brothers levels. We analyze a number of different data representations and how the generated levels fit into the space of human authored Super Mario
Analyzing Massive Machine Maintenance Data in a Computing Cloud

We present a novel framework, CloudView, for storage, processing and analysis of massive machine maintenance data, collected from a large number of sensors embedded in industrial machines, in a cloud computing environment. This paper describes the architecture, design, and implementation of CloudView, and how the proposed framework leverages the parallel computing capability of a computing cloud based on a large-scale distributed batch processing infrastructure that is built of commodity hardware. A case-based reasoning (CBR) approach is adopted for machine fault prediction, where the past cases of failure from a large number of machines are collected in a cloud. A case-base of past cases of failure is created using the global information obtained from a large number of machines. CloudView facilitates organization of sensor data and creation of case-base with global information. Case-base creation jobs are formulated using the MapReduce parallel data processing model. CloudView captures the failure cases across a large number of machines and shares the failure information with a number of local nodes in the form of case-base updates that occur in a time scale of every few hours. At local nodes, the real-time sensor data from a group of machines in the same facility/plant is continuously matched to the cases from the case-base for predicting the incipient faults-this local processing takes a much shorter time of a few seconds. The case-base is updated regularly (in the time scale of a few hours) on the cloud to include new cases of failure, and these case-base updates are pushed from CloudView to the local nodes. Experimental measurements show that fault predictions can be done in real-time (on a timescale of seconds) at the local nodes and massive machine data analysis for case-base creation and updating can be done on a timescale of minutes in the cloud. Our approach, in addition to being the first reported use of the cloud architecture for maintenance data storage, processing and analysis, also evaluates several possible cloud-based architectures that leverage the advantages of the parallel computing capabilities of the cloud to make local decisions with global information efficiently, while avoiding potential data bottlenecks that can occur in getting the maintenance data in and out of the cloud.
A failure mode avoidance approach to reliability

Failure modes are an inevitable consequence of the Product Development (PD) process. They are crucial in their impact on product design since they affect product quality and costs because of the requirement for counter measures, thus potentially imposing high risks on any organization engaged in pro duct development in terms of customer dissatisfaction, financial losses, potential safety litigation, loss of brand reputation etc. Failure Mode Avoidance is a pragmatic methodology to deal with failure modes through counter measures development in the early (that is, information based) phases of the PD process prior to any hardware existing, rather than waiting until the failure modes are naturally observed in the material state later in the process during the production preparation and launch phases, when there is typically an abundance of hardware available to expose the failure modes. This paper focuses to develop an approach to detect and mitigate failure modes systematically in the information based phases of the PD process via a conceptual framework built around Failure Mode and Effects Analysis (FMEA). The FMEA is an important methodology to identify failure modes during product development so that failure modes can be avoided through the development of countermeasures and provides a logical structure (“left to right”) to make the transition from failure mode to counter measure. However the FMEA lacks structure from “top to bottom” since the number of failure modes requiring attention at the outset is usually unknown. This lack of structure often leads to excessively large FM EA documentation with much time devoted to brainstorming a set of potential failure modes. The vision of this work is that the creativity of the engineering team can be devoted to developing counter measures for the failure modes rather than identifying them . The purpose of the paper is to discuss the beginnings of a structured approach to identify failure modes in a systematic way (using the concept of taxonomies), so that the size of the FMEA can be managed.
Expression Profiling of Developing Zebrafish Retinal Cells.

During retinal development, a variety of different types of neurons are produced. Understanding how each of these types of retinal nerve cells is generated is important from a developmental biology perspective. It is equally important if one is interested in how to regenerate cells after an injury or a disease. To gain more insight into how retinal neurons develop in the zebrafish, we performed single-cell mRNA profiling and in situ hybridizations (ISHs) on retinal sections and whole-mount zebrafish. Through the series of ISHs, designed and performed solely by undergraduate students in the laboratory, we were able to retrospectively identify our single-cell mRNA profiles as most likely coming from developing amacrine cells. Further analysis of these profiles will reveal genes that can be mutated using genome editing techniques. Together these studies increase our knowledge of the genes driving development of different cell types in the zebrafish retina.
Kernel auto-encoder for semi-supervised hashing

Hashing-based approaches have gained popularity for large-scale image retrieval in recent years. It has been shown that semi-supervised hashing, which incorporates similarity/dissimilarity information into hash function learning could improve the hashing quality. In this paper, we present a novel kernel-based semi-supervised binary hashing model for image retrieval by taking into account auxiliary information, i.e., similar and dissimilar data pairs in achieving high quality hashing. The main idea is to map the data points into a highly non-linear feature space and then map the non-linear features into compact binary codes such that similar/dissimilar data points have similar/dissimilar hash codes. Empirical evaluations on three benchmark datasets demonstrate the superiority of the proposed method over several existing unsupervised and semi-supervised hash function learning methods.
Comparing comprehension measured by multiple-choice and open-ended questions.

This study compared the nature of text comprehension as measured by multiple-choice format and open-ended format questions. Participants read a short text while explaining preselected sentences. After reading the text, participants answered open-ended and multiple-choice versions of the same questions based on their memory of the text content. The results indicated that performance on open-ended questions was correlated with the quality of self-explanations, but performance on multiple-choice questions was correlated with the level of prior knowledge related to the text. These results suggest that open-ended and multiple-choice format questions measure different aspects of comprehension processes. The results are discussed in terms of dual process theories of text comprehension.
Learning Deep Boltzmann Machines using Adaptive MCMC

When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with many local minima separated by high energy barriers. The commonly used Gibbs sampler tends to get trapped in one local mode, which often results in unstable learning dynamics and leads to poor parameter estimates. In this paper, we concentrate on learning DBM’s using adaptive MCMC algorithms. We first show a close connection between Fast PCD and adaptive MCMC. We then develop a Coupled Adaptive Simulated Tempering algorithm that can be used to better explore a highly multimodal energy landscape. Finally, we demonstrate that the proposed algorithm considerably improves parameter estimates, particularly when learning large-scale DBM’s.
Learning to summarize web image and text mutually

We consider the problem of learning to summarize images by text and visualize text utilizing images, which we call Mutual-Summarization. We divide the web image-text data space into three subspaces, namely pure image space (PIS), pure text space (PTS) and image-text joint space (ITJS). Naturally, we treat the ITJS as a knowledge base.
 For summarizing images by sentence issue, we map images from PIS to ITJS via image classification models and use text summarization on the corresponding texts in ITJS to summarize images. For text visualization problem, we map texts from PTS to ITJS via text categorization models and generate the visualization by choosing the semantic related images from ITJS, where the selected images are ranked by their confidence. In above approaches images are represented by color histograms, dense visual words and feature descriptors at different levels of spatial pyramid; and the texts are generated according to the Latent Dirichlet Allocation (LDA) topic model. Multiple Kernel (MK) methodologies are used to learn classifiers for image and text respectively. We show the Mutual-Summarization results on our newly collected dataset of six big events ("Gulf Oil Spill", "Haiti Earthquake", etc.) as well as demonstrate improved cross-media retrieval performance over existing methods in terms of MAP, Precision and Recall.
Dynamic analysis of a 3-DOF 3-PUU parallel manipulator based on the principle of virtual work

This paper presents a straightforward and systematical methodology for conducting the inverse dynamic analysis of a 3-prismatic-universal-universal (PUU) parallel kinematic manipulator (PKM) through employing the principle of virtual work. The proposed 3-PUU PKM possesses three translational degree-of-freedom (DOF). The position, velocity and acceleration of joints are solved via the inverse kinematic analysis, the link Jacobian matrices which map the velocity of the moving platform into the velocity and angular velocity of struts are derived in the fixed coordinate system. Based on the d'Alembert's form of the principle of virtual work, the applied generalized forces of the whole mechanical system are divided into the applied forces of moving platform, struts, sliders, and motor-coupling-leadscrew, respectively. The joint actuated torques are obtained through eliminating the virtual displacement in the dynamic equations. Furthermore, the inertial term, centrifugal and Coriolis term, and gravitational term of the total joint torque are determined. Simulation for a conical spiral trajectory demonstrates that the total torque is dominated by the inertial term.
Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations

Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP) models has recently received significant attention. We feel that GP priors should be part of the standard toolbox for constructing models relevant to machine learning in the same way as parametric linear models are, and the results in this thesis help to remove some obstacles on the way towards this goal. In the first main chapter, we provide a distribution-free finite sample bound on the difference between generalisation and empirical (training) error for GP classification methods. While the general theorem (the PAC-Bayesian bound) is not new, we give a much simplified and somewhat generalised derivation and point out the underlying core technique (convex duality) explicitly. Furthermore, the application to GP models is novel (to our knowledge). A central feature of this bound is that its quality depends crucially on task knowledge being encoded faithfully in the model and prior distributions, so there is a mutual benefit between a sharp theoretical guarantee and empirically well-established statistical practices. Extensive simulations on real-world classification tasks indicate an impressive tightness of the bound, in spite of the fact that many previous bounds for related kernel machines fail to give non-trivial guarantees in this practically relevant regime. In the second main chapter, sparse approximations are developed to address the problem of the unfavourable scaling of most GP techniques with large training sets. Due to its high importance in practice, this problem has received a lot of attention recently. We demonstrate the tractability and usefulness of simple greedy forward selection with information-theoretic criteria previously used in active learning (or sequential design) and develop generic schemes for automatic model selection with many (hyper)parameters. We suggest two new generic schemes and evaluate some of their variants on large real-world classification and regression tasks. These schemes and their underlying principles (which are clearly stated and analysed) can be applied to obtain sparse approximations for a wide regime of GP models far beyond the special cases we studied here.