nils-herrmann/clean_scirepeval huggingface dataset:
Presupposed Content and Entailments in Natural Language Inference

Previous work has presented an accurate natural logic model for natural language inference. Other work has demonstrated the effectiveness of computing presuppositions for solving natural language inference problems. We extend this work to create a system for correctly computing lexical presuppositions and their interactions within the natural logic framework. The combination allows our system to properly handle presupposition projection from the lexical to the sentential level while taking advantage of the accuracy and coverage of the natural logic system. To solve an inference problem, our system computes a sequence of edits from premise to hypothesis. For each edit the system computes an entailment relation and a presupposition entailment relation. The relations are then separately composed according to a syntactic tree and the semantic properties of its nodes. Presuppositions are projected based on the properties of their syntactic and semantic environment. The edits are then composed and the resulting entailment relations are combined with the presupposition relation to yield an answer to the inference problem.
Exploiting Matrix Dependency for Efficient Distributed Matrix Computation

Distributed matrix computation is a popular approach for many large-scale data analysis and machine learning tasks. However existing distributed matrix computation systems generally incur heavy communication cost during the runtime, which degrades the overall performance. In this paper, we propose a novel matrix computation system, named DMac, which exploits the matrix dependencies in matrix programs for efficient matrix computation in the distributed environment. We decompose each matrix program into a sequence of operations, and reveal the matrix dependencies between operations in the program. We next design a dependency-oriented cost model to select an optimal execution strategy for each operation, and generate a communication efficient execution plan for the matrix computation program. To facilitate the matrix computation in distributed systems, we further divide the execution plan into multiple un-interleaved stages which can run in a distributed cluster with efficient local execution strategy on each worker. The DMac system has been implemented on a popular general-purpose data processing framework, Spark. The experimental results demonstrate that our techniques can significantly improve the performance of a wide range of matrix programs.
Analytic Performance Modeling and Optimization of Live VM Migration

Earlier virtual machine (VM) migration techniques consisted of stop-and-copy: the VM was stopped, its address space was copied to a different physical machine, and the VM was restarted at that machine. Recent VM hypervisors support live VM migration, which allows pages to be copied while the VM is running. If any copied page is dirtied (i.e., modified), it has to be copied again. The process stops when a fraction α of the pages need to be copied. Then, the VM is stopped and the remaining pages are copied. This paper derives a model to compute the downtime, total number of pages copied, and network utilization due to VM migration, as a function of α and other parameters under uniform and non-uniform dirtying rates. The paper also presents a non-linear optimization model to find the value of α that minimizes the downtime subject to network utilization constraints.
Abnormal behavior detection using hybrid agents in crowded scenes

Please save a copy of this file, complete and upload as the " Confirmation of Authorship " file. 1. This manuscript, or a large part of it, has not been published, was not, and is not being submitted to any other journal. If presented at a conference, the conference is identified. If published in conference proceedings, the publication is identified below and substantial justification for re-publication must be presented. 2. All text and graphics, except for those marked with sources, are original works of the authors, and all necessary permissions for publication were secured prior to submission of the manuscript. 3. All authors each made a significant contribution to the research reported and have read and approved the submitted manuscript. 1. The sentence in lines 266-269 is still not properly written. Please correct to something like "Since SFM only uses interaction information between neighbor agents to detect abnormal behavior, it is not productive to detect abnormal behavior when the interaction among neighbors is not represented by the behavior of foreground objects." Answer) We rephrase the statement according to reviewer's comments Before) Since SFM is only used interaction information between neighbor agents to detect abnormal behavior, it is not productive to detect abnormal behavior when the interaction among neighbors is not represented by the behavior of foreground objects. After) Since SFM only uses interaction information between neighbor agents to detect abnormal behavior, it is not productive to detect abnormal behavior when the interaction among neighbors is not represented by the behavior of foreground objects. 2. Please include in the caption of figure 6 the methods to which the red and blue lines refer to. Answer) We modified the caption in figure 6 as follows (a) PETS 2009 dataset (b) UCSD 2009  Results of abnormal behavior detection in two datasets (Red line : SFM, Blue line: our method). • We categorize the behaviors of people into individual and group interactive behavior.  We propose a hybrid agent system that includes static and dynamic agents in a scene. • We represent the behavior of a crowd as a bag of words to detect abnormal behavior. Manuscript [Word or (La)TeX] Click here to download Manuscript [Word or (La)TeX]: paper_revision_final.docx Click here to view linked References Abstract 26 In this paper, we propose a hybrid agent method to detect abnormal behaviors in a crowded 27 scene. In real-life situations, abnormal behavior occurs …
Aesthetics and Entropy III . Aesthetic measures 2

1 Consultant, Maplewood, MN USA 4 * Correspondence: sahyun@infionline.net; 1-(651)-927-9686 5 6  We examined a series of real-world, pictorial photographs with varying 7 characteristics, along with their modification by noise addition and unsharp masking. As 8 response metrics we used three different versions of the aesthetic measure originally 9 proposed by Birkhoff. The first aesthetic measure, which has been used in other studies, 10 and which we used in our previous work as well, showed a preference for the least 11 complex of the images. It provided no justification for noise addition, but did reveal 12 enhancement on unsharp masking. Optimum level of unsharp masking varied with the 13 image, but was predictable from the individual image’s GIF compressibility. We expect this 14 result to be useful for guiding the processing of pictorial photographic imagery. The 15 second aesthetic measure, that of informational aesthetics based on entropy alone failed 16 to provide useful discrimination among the images or the conditions of their modification. 17 A third measure, derived from the concepts of entropy maximization, as well as the 18 hypothesized preference of observers for “simpler”, i.e., more compressible, images, 19 yielded qualitatively the same results as the more traditional version of the measure. 20 Differences among the photographs and the conditions of their modification were more 21 clearly defined with this metric, however. 22
High Power Terahertz and Millimeter-Wave Oscillator Design: A Systematic Approach

A systematic approach to designing high frequency and high power oscillators using activity condition is introduced. This method finds the best topology to achieve frequencies close to the fmax of the transistors. It also determines the maximum frequency of oscillation for a fixed circuit topology, considering the quality factor of the passive components. Using this technique, in a 0.13 μm CMOS process, we design and implement 121 GHz and 104 GHz fundamental oscillators with the output power of -3.5 dBm and -2.7 dBm, respectively. Next, we introduce a novel triple-push structure to realize 256 GHz and 482 GHz oscillators. The 256 GHz oscillator was implemented in a 0.13 μm CMOS process and the output power of -17 dBm was measured. The 482 GHz oscillator generates -7.9 dBm (0.16 mW) in a 65 nm CMOS process.
Short text similarity based on probabilistic topics

In this paper, we propose a new method for measuring the similarity between two short text snippets by comparing each of them with the probabilistic topics. Specifically, our method starts by firstly finding the distinguishing terms between the two short text snippets and comparing them with a series of probabilistic topics, extracted by Gibbs sampling algorithm. The relationship between the distinguishing terms of the short text snippets can be discovered by examining their probabilities under each topic. The similarity between two short text snippets is calculated based on their common terms and the relationship of their distinguishing terms. Extensive experiments on paraphrasing and question categorization show that the proposed method can calculate the similarity of short text snippets more accurately than other methods including the pure TF-IDF measure.
An Adaptive Voltage-Sensor-Based MPPT for Photovoltaic Systems With SEPIC Converter Including Steady-State and Drift Analysis

An adaptive voltage-sensor-based maximum power point tracking algorithm employing a variable scaling factor for a single-ended primary-inductance converter is presented. In this method, only a voltage divider circuit is used to sense the photovoltaic (PV) panel voltage. This method can effectively improve both transient and steady-state performance by varying the scaling factor as compared with the fixed step size and adaptive step size with fixed scaling factor. For sudden change in solar insolation or in start-up, this method leads to faster tracking, whereas in steady state, it leads to lower oscillations around maximum power point. The steady-state behavior and drift phenomena are also addressed in this paper to determine the tracking efficiency. The duty cycle is generated directly without using any proportional-integral control loop to simplify the control circuit. MATLAB/Simulink is used for simulation studies, and a microcontroller is used as a digital platform to implement the proposed algorithm for experimental validation. The proposed system is implemented and tested successfully on a PV panel in the laboratory.
A 69.8 dB SNDR 3rd-order Continuous Time Delta-Sigma Modulator with an Ultimate Low Power Tuning System for a Worldwide Digital TV-Receiver

This paper presents a 3rd-order continuous time delta-sigma modulator for a worldwide digital TV-receiver whose SNDR is 69.8 dB. An ultimate low power tuning system using RC-relaxation oscillator is developed in order to achieve high yield against PVT variations. A 3rd-order modulator with modified single opamp resonator contributes to cost reduction by realizing very compact circuit. The mechanism to occur 2nd-order harmonic distortion at current feedback DAC was analyzed and a reduction scheme of the distortion enabled the modulator to achieved FOM of 0.18 pJ/conv-step.
Anticipating the Future by Constructing Human Activities using Object Affordances

An important aspect of human perception is anticipation and anticipating which activities will a human do next (and how to do them) in useful for many applications, for example, anticipation enables an assistive robot to plan ahead for reactive responses in the human environments. In this work, we present a constructive approach for generating various possible future human activities by reasoning about the rich spatial-temporal relations through object affordances. We represent each possible future using an anticipatory temporal conditional random field (ATCRF) where we sample the nodes and edges corresponding to future object trajectories and human poses from a generative model. We then represent the distribution over the potential futures using a set of constructed ATCRF particles. In extensive evaluation on CAD-120 human activity RGB-D dataset, for new subjects (not seen in the training set), we obtain an activity anticipation accuracy (defined as whether one of top three predictions actually happened) of 75.4%, 69.2% and 58.1% for an anticipation time of 1, 3 and 10 seconds respectively.
Digital forensic framework using feedback and case history keeper

Cyber crime investigation is the integration of two technologies named theoretical methodology and second practical tools. First is the theoretical digital forensic methodology that encompasses the steps to investigate the cyber crime. And second technology is the practically development of the digital forensic tool which sequentially and systematically analyze digital devices to extract the evidence to prove the crime. This paper explores the development of digital forensic framework, combine the advantages of past twenty five forensic models and generate a algorithm to create a new digital forensic model. The proposed model provides the following advantages, a standardized method for investigation, the theory of model can be directly convert into tool, a history lookup facility, cost and time minimization, applicable to any type of digital crime investigation.
SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery

In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or samplingbased methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.
Bayesian robot system identification with input and output noise

For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods.
The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation

F or D walkingcontr ol of a biped robot we analyze the dynamics of a three dimensional inverted pendu lum in which motion is constr aine d to move along an arbitr arily de ned plane This analysis leads us a sim ple line ar dynamics the Thr ee Dimensional Linear In verte d Pendulum Mode D LIPM Geometric nature of trajectories under the D LIPM and a method for walking pattern gener ationare discusse d A simula tion r esult of a walking contr ol using a d o f biped robot model is also shown
An Efficient Character-Level Neural Machine Translation

Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation. However, the use of large vocabulary becomes the bottleneck in both training and improving the performance. In this paper, we propose an efficient architecture to train a deep character-level neural machine translation by introducing a decimator and an interpolator. The decimator is used to sample the source sequence before encoding while the interpolator is used to resample after decoding. Such a deep model has two major advantages. It avoids the large vocabulary issue radically; at the same time, it is much faster and more memory-efficient in training than conventional character-based models. More interestingly, our model is able to translate the misspelled word like human beings.
Hierarchical Multi-Agent Reinforcement Learning through Communicative Actions for Human-Robot Collaboration

As we expect robots to start moving from working in isolated industry settings into human populated environments, our need to develop suitable learning algorithms for the latter increases. Human-robot collaboration is a particular area that has tremendous gains from endowing a robot with such learning capabilities, focusing on robots that can work side-by-side with a human and provide supportive behaviors throughout a task executed by the human worker. In this paper, we propose a framework based on hierarchical multi-agent reinforcement learning that considers the human as an “expert” agent in the system—an agent whose actions we cannot control but whose actions, jointly with the robot’s actions, impact the state of the task. Our framework aims to provide the learner (the robot) with a way of learning how to provide supportive behaviors to the expert agent (the person) during a complex task. The robot employs communicative actions to interactively learn from the expert agent at key points during the task. We use a hierarchical approach in order to integrate the communicative actions in the multi-agent reinforcement learning framework and allow for simultaneously learning the quality of performing different supportive behaviors for particular combinations of task states and expert agent actions. In this paper, we present our proposed framework, detail the motion capture system data collection we performed in order to learn about the task states and characterize the expert agent’s actions, and discuss how we can apply the framework to our human-robot collaboration scenario.
The Business Case for Corporate Social Responsibility : A Review of Concepts , Research and Practiceijmr

In this review, the primary subject is the ‘business case’ for corporate social responsibility (CSR). The business case refers to the underlying arguments or rationales supporting or documenting why the business community should accept and advance the CSR ‘cause’. The business case is concerned with the primary question: What do the business community and organizations get out of CSR? That is, how do they benefit tangibly from engaging in CSR policies, activities and practices? The business case refers to the bottom-line financial and other reasons for businesses pursuing CSR strategies and policies. In developing this business case, the paper first provides some historical background and perspective. In addition, it provides a brief discussion of the evolving understandings of CSR and some of the long-established, traditional arguments that have been made both for and against the idea of business assuming any responsibility to society beyond profit-seeking and maximizing its own financial wellbeing. Finally, the paper addresses the business case in more detail. The goal is to describe and summarize what the business case means and to review some of the concepts, research and practice that have come to characterize this developing idea.
"Finding our time": Predicting students' time management in online collaborative groupwork

The aim of this study is to propose and empirically examine multilevel models of students’ time management in online collaborative groupwork. Studentand group-level predictors of groupwork time management were analyzed in a survey of 204 graduate students from 61 groups in the Southeast of U.S. Results from the multilevel analyses revealed that most of the variance in time management occurred at the student level, with peer-oriented reasons being the only significant predictor at the group level. Results further revealed that groupwork time management was positively related to feedback and students’ efforts in arranging the study environment.
Determining Surface Orientations of Specular Surfaces by Using the Photometric Stereo Method

The orientation of patches on the surface of an object can be determined from multiple images taken with different illumination, but from the same viewing position. The method, referred to as photometric stereo, can be implemented using table lookup based on numerical inversion of reflectance maps. Here we concentrate on objects with specularly reflecting surfaces, since these are of importance in industrial applications. Previous methods, intended for diffusely reflecting surfaces, employed point source illumination, which is quite unsuitable in this case. Instead, we use a distributed light source obtained by uneven illumination of a diffusely reflecting planar surface. Experimental results are shown to verify analytic expressions obtained for a method employing three light source distributions.
Syntax-Tree Regular Expression Based DFA FormalConstruction

Compiler is a program whose functionality is to translate a computer program written in source language into an equivalent machine code. Compiler construction is an advanced research area because of its size and complexity. The source codes are in higher level languages which are usually complex and, consequently, increase the level of abstraction. Due to such reasons, design and construction of error free compiler is a challenge of the twenty first century. Verification of a source program does not guarantee about correctness of code generated because the bugs in compiler may lead to an incorrect target program. Therefore, verification of compiler is more important than verifying the source programs. Lexical analyzer is a main phase of compiler used for scanning input and grouping into sequence of tokens. In this paper, formal construction of deterministic finite automata (DFA) based on regular expression is presented as a part of lexical analyzer. At first, syntax tree is described based on the augmented regular expression. Then formal description of important operators, checking null-ability and computing first and last positions of internal nodes of the tree is described. In next, the transition diagram is described from the follow positions and converted into deterministic finite automata by defining a relationship among syntax tree, transition diagram and DFA. Formal specification of the procedure is described using Z notation and model analysis is provided using Z/Eves toolset.
Star Coordinates: A Multi-dimensional Visualization Technique with Uniform Treatment of Dimensions

Visualizing multi-dimensional data has tremendous effects on science, engineering, and business decisionmaking. A new visualization technique called Star Coordinates is presented to support users in early stages of their visual thinking activities. Star Coordinates arranges coordinates on a circle sharing the same origin at the center. It uses simply points to represent data, treating each dimension uniformly at the cost of coarse representation. Current implementation of Star Coordinates provided valuable insight on several real data sets for cluster discovery and multifactor analysis tasks. The work on Star Coordinates will continue on developing advanced transformations that will improve data understanding in multi-dimensions.