nils-herrmann/clean_scirepeval huggingface dataset:
Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence

Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control. Hand gestures provide a separate complementary modality to speech for expressing ones ideas. Information associated with hand gestures in a conversation is degree, discourse structure, spatial and temporal structure. The approaches present can be mainly divided into Data-Glove Based and Vision Based approaches. An important face feature point is the nose tip. Since nose is the highest protruding point from the face. Besides that, it is not affected by facial expressions. Another important function of the nose is that it is able to indicate the head pose. Knowledge of the nose location will enable us to align an unknown 3D face with those in a face database. Eye detection is divided into eye position detection and eye contour detection. Existing works in eye detection can be classified into two major categories: traditional image-based passive approaches and the active IR based approaches. The former uses intensity and shape of eyes for detection and the latter works on the assumption that eyes have a reflection under near IR illumination and produce bright/dark pupil effect. The traditional methods can be broadly classified into three categories: template based methods, appearance based methods and feature based methods. The purpose of this paper is to compare various human Gesture recognition systems for interfacing machines directly to human wits without any corporeal media in an ambient environment.
A Stakeholder Win-Win Approach to Software Engineering Education

Barry Boehm a, Alexander Egyed a, Dan Port a, Archita Shah a, Julie Kwan b and Ray Madachy c a Center for Software Engineering, Henry Salvatori Computer Science 300, University of Southern California, Los Angeles, CA 90089-0781, USA E-mail: {boehm,aegyed,dport,ashah}@sunset.usc.edu b USC University Libraries, University of Southern California, Los Angeles, CA 90089-1421, USA E-mail: jkwan@calvin.usc.edu c Litton Data Systems, Guidance & Control Systems, 5500 Canoga Avenue, Woodland Hills, CA 91367-6698, USA E-mail: madachy@sunset.usc.edu
On Vectorization of Deep Convolutional Neural Networks for Vision Tasks

We recently have witnessed many ground-breaking results in machine learning and computer vision, generated by using deep convolutional neural networks (CNN). While the success mainly stems from the large volume of training data and the deep network architectures, the vector processing hardware (e.g. GPU) undisputedly plays a vital role in modern CNN implementations to support massive computation. Though much attention was paid in the extent literature to understand the algorithmic side of deep CNN, little research was dedicated to the vectorization for scaling up CNNs. In this paper, we studied the vectorization process of key building blocks in deep CNNs, in order to better understand and facilitate parallel implementation. Key steps in training and testing deep CNNs are abstracted as matrix and vector operators, upon which parallelism can be easily achieved. We developed and compared six implementations with various degrees of vectorization with which we illustrated the impact of vectorization on the speed of model training and testing. Besides, a unified CNN framework for both high-level and low-level vision tasks is provided, along with a vectorized Matlab implementation with state-of-the-art speed performance.
Anonymity for Bitcoin with accountable mixes ( Full version )

We propose Mixcoin, a protocol to facilitate anonymous payments using the Bitcoin currency system. We build on the emergent phenomenon of currency mixes, adding an accountability mechanism to expose theft. Unlike other proposals to improve anonymity in Bitcoin, our scheme can be deployed immediately with no changes to Bitcoin itself. We demonstrate that incentives of mixes and clients can be aligned to ensure that rational mixes will not steal from clients. We contrast mixing for financial anonymity with better-studied communication mixes, demonstrating important and subtle new attacks.
Resilience and sustainable development: building adaptive capacity in a world of transformations.

Emerging recognition of two fundamental errors underpinning past polices for natural resource issues heralds awareness of the need for a worldwide fundamental change in thinking and in practice of environmental management. The first error has been an implicit assumption that ecosystem responses to human use are linear, predictable and controllable. The second has been an assumption that human and natural systems can be treated independently. However, evidence that has been accumulating in diverse regions all over the world suggests that natural and social systems behave in nonlinear ways, exhibit marked thresholds in their dynamics, and that social-ecological systems act as strongly coupled, complex and evolving integrated systems. This article is a summary of a report prepared on behalf of the Environmental Advisory Council to the Swedish Government, as input to the process of the World Summit on Sustainable Development (WSSD) in Johannesburg, South Africa in 26 August 4 September 2002. We use the concept of resilience--the capacity to buffer change, learn and develop--as a framework for understanding how to sustain and enhance adaptive capacity in a complex world of rapid transformations. Two useful tools for resilience-building in social-ecological systems are structured scenarios and active adaptive management. These tools require and facilitate a social context with flexible and open institutions and multi-level governance systems that allow for learning and increase adaptive capacity without foreclosing future development options.
Adding High-Precision Links to Wikipedia

Wikipedia’s link structure is a valuable resource for natural language processing tasks, but only a fraction of the concepts mentioned in each article are annotated with hyperlinks. In this paper, we study how to augment Wikipedia with additional high-precision links. We present 3W, a system that identifies concept mentions in Wikipedia text, and links each mention to its referent page. 3W leverages rich semantic information present in Wikipedia to achieve high precision. Our experiments demonstrate that 3W can add an average of seven new links to each Wikipedia article, at a precision of 0.98.
Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block-separable constraints. Despite its lower iteration cost, we show that it achieves a similar convergence rate in duality gap as the full FrankWolfe algorithm. We also show that, when applied to the dual structural support vector machine (SVM) objective, this yields an online algorithm that has the same low iteration complexity as primal stochastic subgradient methods. However, unlike stochastic subgradient methods, the block-coordinate FrankWolfe algorithm allows us to compute the optimal step-size and yields a computable duality gap guarantee. Our experiments indicate that this simple algorithm outperforms competing structural SVM solvers.
Medical Image Deep Learning with Hospital PACS Dataset

The use of Convolutional Neural Networks (CNN) in natural im age classification systems has produced very impressive results. Combined wit h the inherent nature of medical images that make them ideal for deep-learning, fu rther application of such systems to medical image classification holds much prom ise. However, the usefulness and potential impact of such a system can be compl etely negated if it does not reach a target accuracy. In this paper, we present a s tudy on determining the optimum size of the training data set necessary to achiev e igh classification accuracy with low variance in medical image classification s ystems. The CNN was applied to classify axial Computed Tomography (CT) imag es into six anatomical classes. We trained the CNN using six different sizes of training data set ( 5, 10, 20, 50, 100, and200) and then tested the resulting system with a total of 6000 CT images. All images were acquired from the Massachusetts G eneral Hospital (MGH) Picture Archiving and Communication System (PACS). U sing this data, we employ the learning curve approach to predict classificat ion ccuracy at a given training sample size. Our research will present a general me thodology for determining the training data set size necessary to achieve a cert in target classification accuracy that can be easily applied to other problems within such systems.
Nonlinear decoding of a complex movie from the mammalian retina

Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed "pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains.
Learning-Assisted Automated Reasoning with Flyspeck

The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the Flyspeck proofs, producing an AI system capable of proving a wide range of mathematical conjectures automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39 % of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided.
A comparison of different design methods for the multiresonant LLC converter with capacitive output filter

Faced with ever shorter product cycles, SMPS designers require fast and reliable modeling tools. In case of resonant converters the derivation of the exact solution is complex and time-consuming. Thus, simplified calculation methods are attractive. However, the reduced accuracy of these approximation methods has to be taken into account carefully. In this paper, the concepts of the First Harmonic Approximation (FHA) and the extended First Harmonic Approximation (eFHA) of the multiresonant LLC converter with capacitive output filter are discussed. Furthermore, the accuracy of both approximation methods is evaluated in detail with respect to the results of the exact solution in the time domain.
A practical flow-sensitive and context-sensitive C and C++ memory leak detector

This paper presents a static analysis tool that can automatically find memory leaks and deletions of dangling pointers in large C and C++ applications.We have developed a type system to formalize a practical ownership model of memory management. In this model, every object is pointed to by one and only one owning pointer, which holds the exclusive right and obligation to either delete the object or to transfer the right to another owning pointer. In addition, a pointer-typed class member field is required to either always or never own its pointee at public method boundaries. Programs satisfying this model do not leak memory or delete the same object more than once.We have also developed a flow-sensitive and context-sensitive algorithm to automatically infer the likely ownership interfaces of methods in a program. It identifies statements inconsistent with the model as sources of potential leaks or double deletes. The algorithm is sound with respect to a large subset of the C and C++ language in that it will report all possible errors. It is also practical and useful as it identifies those warnings likely to correspond to errors and helps the user understand the reported errors by showing them the assumed method interfaces.Our techniques are validated with an implementation of a tool we call Clouseau. We applied Clouseau to a suite of applications: two web servers, a chat client, secure shell tools, executable object manipulation tools, and a compiler. The tool found a total of 134 serious memory errors in these applications. The tool analyzes over 50K lines of C++ code in about 9 minutes on a 2 GHz Pentium 4 machine and over 70K lines of C code in just over a minute.
An Attentional Neural Conversation Model with Improved Specificity

In this paper we propose a neural conversation model for conducting dialogues. We demonstrate the use of this model to generate help desk responses, where users are asking questions about PC applications. Our model is distinguished by two characteristics. First, it models intention across turns with a recurrent network, and incorporates an attention model that is conditioned on the representation of intention. Secondly, it avoids generating nonspecific responses by incorporating an IDF term in the objective function. The model is evaluated both as a pure generation model in which a help-desk response is generated from scratch, and as a retrieval model with performance measured using recall rates of the correct response. Experimental results indicate that the model outperforms previously proposed neural conversation architectures, and that using specificity in the objective function significantly improves performances for both generation and retrieval.
A new control plane for 5G network architecture with a case study on unified handoff, mobility, and routing management

The tremendous growth in wireless Internet use is showing no signs of slowing down. Existing cellular networks are starting to be insufficient in meeting this demand, in part due to their inflexible and expensive equipment as well as complex and non-agile control plane. Software-defined networking is emerging as a natural solution for next generation cellular networks as it enables further network function virtualization opportunities and network programmability. In this article, we advocate an all-SDN network architecture with hierarchical network control capabilities to allow for different grades of performance and complexity in offering core network services and provide service differentiation for 5G systems. As a showcase of this architecture, we introduce a unified approach to mobility, handoff, and routing management and offer connectivity management as a service (CMaaS). CMaaS is offered to application developers and over-the-top service providers to provide a range of options in protecting their flows against subscriber mobility at different price levels.
Cascade R-CNN: Delving Into High Quality Object Detection

In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code is available at https://github.com/zhaoweicai/cascade-rcnn.
Event detection and popularity prediction in microblogging

As one of the most influential social media platforms, microblogging is becoming increasingly popular in the last decades. Each day a large amount of events appear and spread in microblogging. The spreading of events and corresponding comments on them can greatly influence the public opinion. It is practical important to discover new emerging events in microblogging and predict their future popularity. Traditional event detection and information diffusion models cannot effectively handle our studied problem, because most existing methods focus only on event detection but ignore to predict their future trend. In this paper, we propose a new approach to detect burst novel events and predict their future popularity simultaneously. Specifically, we first detect events from online microblogging stream by utilizing multiple types of information, i.e., term frequency, and user's social relation. Meanwhile, the popularity of detected event is predicted through a proposed diffusion model which takes both the content and user information of the event into account. Extensive evaluations on two real-world datasets demonstrate the effectiveness of our approach on both event detection and their popularity
Hybrid Dialog State Tracker with ASR Features

This paper presents a hybrid dialog state tracker enhanced by trainable Spoken Language Understanding (SLU) for slotfilling dialog systems. Our architecture is inspired by previously proposed neuralnetwork-based belief-tracking systems. In addition we extended some parts of our modular architecture with differentiable rules to allow end-to-end training. We hypothesize that these rules allow our tracker to generalize better than pure machinelearning based systems. For evaluation we used the Dialog State Tracking Challenge (DSTC) 2 dataset a popular belief tracking testbed with dialogs from restaurant information system. To our knowledge, our hybrid tracker sets a new stateof-the-art result in three out of four categories within the DSTC2.
RECENT ADVANCES IN PERSONAL RECOMMENDER SYSTEMS

In the past years we have witnessed an explosive growth of the data and information on the World Wide Web, which makes it difficult for normal users to find the information that they are interested in. On the other hand, the majority of the data and resources are very unpopular, which can be considered as “hidden information”, and are very difficult to find. By building a bridge between the users and the objects and constructing their similarities, the Personal Recommender System (PRS) can recommend the objects that the users are potentially interested in. PRS plays an important role in not only social and economic life but also scientific analysis. The interdisciplinary PRS attracts attention from the communities of information science, computational mathematics, statistical physics, management science, and consumer behaviors, etc. In fact, PRS is one of the most efficient tools to solve the information overload problem. According to the recommendation algorithms, we introduce four typical systems, including the collaborating filtering system, the content-based system, the structure-based system, and the hybrid system. In addition, some improved algorithms are proposed to overcome the limitations of traditional systems. This review article may shed some light on the study of PRS from different backgrounds.
5nm FinFET Standard Cell Library Optimization and Circuit Synthesis in Near-and Super-Threshold Voltage Regimes

FinFET device has been proposed as a promising substitute for the traditional bulk CMOS-based device at the nanoscale, due to its extraordinary properties such as improved channel controllability, high ON/OFF current ratio, reduced short-channel effects, and relative immunity to gate line-edge roughness. In addition, the near-ideal subthreshold behavior indicates the potential application of FinFET circuits in the near-threshold supply voltage regime, which consumes an order of magnitude less energy than the regular strong-inversion circuits operating in the super-threshold supply voltage regime. This paper presents a design flow of creating standard cells by using the FinFET 5nm technology node, including both near-threshold and super-threshold operations, and building a Liberty-format standard cell library. The circuit synthesis results of various combinational and sequential circuits based on the 5nm FinFET standard cell library show up to 40X circuit speed improvement and three orders of magnitude energy reduction compared to those of 45nm bulk CMOS technology.