nils-herrmann/clean_scirepeval huggingface dataset:
Randomness in neural networks: an overview

Neural networks, as powerful tools for data mining and knowledge engineering, can learn from data to build feature-based classifiers and nonlinear predictive models. Training neural networks involves the optimization of nonconvex objective functions, and usually, the learning process is costly and infeasible for applications associated with data streams. A possible, albeit counterintuitive, alternative is to randomly assign a subset of the networks’ weights so that the resulting optimization task can be formulated as a linear least-squares problem. This methodology can be applied to both feedforward and recurrent networks, and similar techniques can be used to approximate kernel functions. Many experimental results indicate that such randomized models can reach sound performance compared to fully adaptable ones, with a number of favorable benefits, including (1) simplicity of implementation, (2) faster learning with less intervention from human beings, and (3) possibility of leveraging overall linear regression and classification algorithms (e.g., l1 norm minimization for obtaining sparse formulations). This class of neural networks attractive and valuable to the data mining community, particularly for handling large scale data mining in real-time. However, the literature in the field is extremely vast and fragmented, with many results being reintroduced multiple times under different names. This overview aims to provide a self-contained, uniform introduction to the different ways in which randomization can be applied to the design of neural networks and kernel functions. A clear exposition of the basic framework underlying all these approaches helps to clarify innovative lines of research, open problems, and most importantly, foster the exchanges of well-known results throughout different communities.
A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.
Performance of Feedforward Current Regulators for Field-Oriented Induction Machine Controllers

To achieve instantaneous control of induction motor torque using field-orientation techniques, it is necessary that the phase currents be controlled to maintain precise instantaneous relationships. Failure to do so results in a noticeable degradation in torque response. Most of the currently used approaches to achieve this control employ classical control strategies which are only correct for steady-state conditions. A modern control theory approach which circumvents these limitations is developed. The approach uses a state-variable feedback control model of the field-oriented induction machine. This state-variable controller is shown to be intrinsically more robust than PI regulators. Experimental verification of the performance of this state-variable control strategy in achieving current-loop performance and torque control at high operating speeds is included.
The Inverse Regression Topic Model

Taddy (2013) proposed multinomial inverse regression (MNIR) as a new model of annotated text based on the influence of metadata and response variables on the distribution of words in a document. While effective, MNIR has no way to exploit structure in the corpus to improve its predictions or facilitate exploratory data analysis. On the other hand, traditional probabilistic topic models (like latent Dirichlet allocation) capture natural heterogeneity in a collection but do not account for external variables. In this paper, we introduce the inverse regression topic model (IRTM), a mixed-membership extension of MNIR that combines the strengths of both methodologies. We present two inference algorithms for the IRTM: an efficient batch estimation algorithm and an online variant, which is suitable for large corpora. We apply these methods to a corpus of 73K Congressional press releases and another of 150K Yelp reviews, demonstrating that the IRTM outperforms both MNIR and supervised topic models on the prediction task. Further, we give examples showing that the IRTM enables systematic discovery of in-topic lexical variation, which is not possible with previous supervised topic models.
Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms

The overall focus of this research is to demonstrate the savings potential generated by the integration of the design of strategic global supply chain networks with the determination of tactical production–distribution allocations and transfer prices. The logistics systems design problem is defined as follows: given a set of potential suppliers, potential manufacturing facilities, and distribution centers with multiple possible configurations, and customers with deterministic demands, determine the configuration of the production–distribution system and the transfer prices between various subsidiaries of the corporation such that seasonal customer demands and service requirements are met and the after tax profit of the corporation is maximized. The after tax profit is the difference between the sales revenue minus the total system cost and taxes. The total cost is defined as the sum of supply, production, transportation, inventory, and facility costs. Two models and their associated solution algorithms will be introduced. The savings opportunities created by designing the system with a methodology that integrates strategic and tactical decisions rather than in a hierarchical fashion are demonstrated with two case studies. The first model focuses on the setting of transfer prices in a global supply chain with the objective of maximizing the after tax profit of an international corporation. The constraints mandated by the national taxing authorities create a bilinear programming formulation. We will describe a very efficient heuristic iterative solution algorithm, which alternates between the optimization of the transfer prices and the material flows. Performance and bounds for the heuristic algorithms will be discussed. The second model focuses on the production and distribution allocation in a single country system, when the customers have seasonal demands. This model also needs to be solved as a subproblem in the heuristic solution of the global transfer price model. The research develops an integrated design methodology based on primal decomposition methods for the mixed integer programming formulation. The primal decomposition allows a natural split of the production and transportation decisions and the research identifies the necessary information flows between the subsystems. The primal decomposition method also allows a very efficient solution algorithm for this general class of large mixed integer programming models. Data requirements and solution times will be discussed for a real life case study in the packaging industry.  E-mail address: marc.goetschalckx@isye.gatech.edu (M. Goetschalckx). 0377-2217/02/$ see front matter 2002 00142-X
KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers

This paper describes the KeLP system participating in the SemEval-2016 Community Question Answering (cQA) task. The challenge tasks are modeled as binary classification problems: kernel-based classifiers are trained on the SemEval datasets and their scores are used to sort the instances and produce the final ranking. All classifiers and kernels have been implemented within the Kernel-based Learning Platform called KeLP. Our primary submission ranked first in Subtask A, third in Subtask B and second in Subtask C. These ranks are based on MAP, which is the referring challenge system score. Our approach outperforms all the other systems with respect to all the other challenge metrics.
An extractive text summarization technique for Bengali document(s) using K-means clustering algorithm

Text summarization, a field of data mining, is very important for developing various real-life applications. Many techniques have been developed for summarizing English text(s). But, a few attempts have been made for Bengali text because of its some multifaceted structure. This paper presents a method for text summarization which extracts important sentences from a single or multiple Bengali documents. The input document(s) should be pre-processed by tokenization, stemming operation etc. Then, word score is calculated by Term-Frequency/Inverse Document Frequency (TF/IDF) and sentence score is determined by summing up its constituent words' scores with its position. Cue and skeleton words have also been considered to calculate the sentence score. For single or multiple documents, K-means clustering algorithm has been applied to produce the final summary. The experimental result shows satisfactory outputs in comparison to the existing approaches possessing linear run time complexity.
It Takes Two to Tango: Towards Theory of AI's Mind

Theory of Mind is the ability to attribute mental states (beliefs, intents, knowledge, perspectives, etc.) to others and recognize that these mental states may differ from one’s own. Theory of Mind is critical to effective communication and to teams demonstrating higher collective performance. To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, there has been much emphasis on research to make AI more accurate, and (to a lesser extent) on having it better understand human intentions, tendencies, beliefs, and contexts. The latter involves making AI more human-like and having it develop a theory of our minds. In this work, we argue that for human-AI teams to be effective, humans must also develop a theory of AI’s mind – get to know its strengths, weaknesses, beliefs, and quirks. We instantiate these ideas within the domain of Visual Question Answering (VQA). We find that using just a few examples (50), lay people can be trained to better predict responses and oncoming failures of a complex VQA model. Surprisingly, we find that having access to the model’s internal states – its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image – do not help people better predict its behavior.
A Survey of Predictive Modeling on Imbalanced Domains

Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.
HeidelTime: High Quality Rule-Based Extraction and Normalization of Temporal Expressions

Different types •Date: On May 22, 1995, Farkas was ... •Time: ... in Brownsville around 7:15 p.m. •Duration: He spent six days abroad ... •Set: ... for liver transplants each year ... Different occurrences in documents • explicit easy to normalize • implicit knowledge is needed • relative reference time is needed (& additional information) Annotation scheme •TimeML: ISO standard for temporal annotation (Timex3)  Main Challenges
Survey of Polygonal Surface Simplification Algorithms

This paper surveys methods for simplifying and approximating polygonal surfaces. A polygonal surface is a piecewiselinear surface in 3-D defined by a set of polygons; typically a set of triangles. Methods from computer graphics, computer vision, cartography, computational geometry, and other fields are classified, summarized, and compared both practically and theoretically. The surface types range from height fields (bivariate functions), to manifolds, to nonmanifold self-intersecting surfaces. Piecewise-linear curve simplification is also briefly surveyed. This work was supported by ARPA contract F19628-93-C-0171 and NSF Young Investigator award CCR-9357763. Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.
Accurate Latency-based Congestion Feedback for Datacenters

The nature of congestion feedback largely governs the behavior of congestion control. In datacenter networks, where RTTs are in hundreds of microseconds, accurate feedback is crucial to achieve both high utilization and low queueing delay. Proposals for datacenter congestion control predominantly leverage ECN or even explicit innetwork feedback (e.g., RCP-type feedback) to minimize the queuing delay. In this work we explore latency-based feedback as an alternative and show its advantages over ECN. Against the common belief that such implicit feedback is noisy and inaccurate, we demonstrate that latencybased implicit feedback is accurate enough to signal a single packet’s queuing delay in 10 Gbps networks. DX enables accurate queuing delay measurements whose error falls within 1.98 and 0.53 microseconds using software-based and hardware-based latency measurements, respectively. This enables us to design a new congestion control algorithm that performs fine-grained control to adjust the congestion window just enough to achieve very low queuing delay while attaining full utilization. Our extensive evaluation shows that 1) the latency measurement accurately reflects the one-way queuing delay in single packet level; 2) the latency feedback can be used to perform practical and fine-grained congestion control in high-speed datacenter networks; and 3) DX outperforms DCTCP with 5.33x smaller median queueing delay at 1 Gbps and 1.57x at 10 Gbps.
The Decision to Share Information and Rumors: Examining the Role of Motivation in an Online Discussion Forum

The focus of this study is to examine the motivations of online community members to share information and rumors. We investigated an online community of interest, the members of which voluntarily associate and communicate with people with similar interests. Community members, posters and lurkers alike, were surveyed on the influence of extrinsic and intrinsic motivations, as well as normative influences, on their willingness to share information and rumors with others. The results indicated that posters and lurkers are differently motivated by intrinsic factors to share, and that extrinsic rewards like improved reputation and status-building within the community are motivating factors for rumor mongering. The results are discussed and future directions for this area of research are offered.
PeachPy meets Opcodes: direct machine code generation from Python

We introduce Opcodes, a Python package which presents x86 and x86-64 instruction sets as a set of high-level objects. Opcodes provides information about instruction names, implicit and explicit operands, and instruction encoding. We use the Opcodes package to auto-generate instruction classes for PeachPy, an x86-64 assembler embedded in Python, and enable new functionality.
 The new PeachPy functionality lets low-level optimization experts write high-performance assembly kernels in Python, load them as callable Python functions, test the kernels using numpy and generate object files for Windows, Linux, and Mac OS X entirely within Python. Additionally, the new PeachPy can generate and run assembly code inside Chromium-based browsers by leveraging Native Client technology. Beyond that, PeachPy gained ability to target Google Go toolchain, by generating either source listing for Go assembler, or object files that can be linked with Go toolchain.
 With backends for Windows, Linux, Mac OS X, Native Client, and Go, PeachPy is the most portable way to write high-performance kernels for x86-64 architecture.
Audio-visual integration in multimodal communication

In this paper, we review recent research that examines audiovisual integration in multimodal communication. The topics include bimodality in human speech, human and automated lip reading, facial animation, lip synchronization, joint audiovideo coding, and bimodal speaker verification. We also study the enabling technologies for these research topics, including automatic facial-feature tracking and audio-to-visual mapping. Recent progress in audio-visual research shows that joint processing of audio and video provides advantages that are not available when the audio and video are processed independently.
Nonline-of-sight error mitigation in mobile location

The location of mobile terminals has received considerable attention in the recent years. The performance of mobile location systems is limited by errors primarily caused by nonline-of-sight (NLOS) propagation conditions. We investigate the NLOS error identification and correction techniques for mobile user location in wireless cellular systems. Based on how much a priori knowledge of the NLOS error is available, two NLOS mitigation algorithms are proposed. Simulation results demonstrate that with the prior information database, the location estimate can be obtained with good accuracy even in severe NLOS propagation conditions.
A Smart Kitchen for Ambient Assisted Living

The kitchen environment is one of the scenarios in the home where users can benefit from Ambient Assisted Living (AAL) applications. Moreover, it is the place where old people suffer from most domestic injuries. This paper presents a novel design, implementation and assessment of a Smart Kitchen which provides Ambient Assisted Living services; a smart environment that increases elderly and disabled people's autonomy in their kitchen-related activities through context and user awareness, appropriate user interaction and artificial intelligence. It is based on a modular architecture which integrates a wide variety of home technology (household appliances, sensors, user interfaces, etc.) and associated communication standards and media (power line, radio frequency, infrared and cabled). Its software architecture is based on the Open Services Gateway initiative (OSGi), which allows building a complex system composed of small modules, each one providing the specific functionalities required, and can be easily scaled to meet our needs. The system has been evaluated by a large number of real users (63) and carers (31) in two living labs in Spain and UK. Results show a large potential of system functionalities combined with good usability and physical, sensory and cognitive accessibility.
Video SnapCut: robust video object cutout using localized classifiers

Although tremendous success has been achieved for interactive object cutout in still images, accurately extracting dynamic objects in video remains a very challenging problem. Previous video cutout systems present two major limitations: (1) reliance on global statistics, thus lacking the ability to deal with complex and diverse scenes; and (2) treating segmentation as a global optimization, thus lacking a practical workflow that can guarantee the convergence of the systems to the desired results.
 We present Video SnapCut, a robust video object cutout system that significantly advances the state-of-the-art. In our system segmentation is achieved by the collaboration of a set of local classifiers, each adaptively integrating multiple local image features. We show how this segmentation paradigm naturally supports local user editing and propagates them across time. The object cutout system is completed with a novel coherent video matting technique. A comprehensive evaluation and comparison is presented, demonstrating the effectiveness of the proposed system at achieving high quality results, as well as the robustness of the system against various types of inputs.
Enhancing Penetration Testing with Attack Signatures and Interface Monitoring for the Detection of Injection Vulnerabilities in Web Services

Web services are often deployed with critical software bugs that may be maliciously exploited. Developers often trust on penetration testing tools to detect those vulnerabilities but the effectiveness of such technique is limited by the lack of information on the internal state of the tested services. This paper proposes a new approach for the detection of injection vulnerabilities in web services. The approach uses attack signatures and interface monitoring to increase the visibility of the penetration testing process, yet without needing to access web service's internals (as these are frequently not available). To demonstrate the feasibility of the approach we implemented a prototype tool to detect SQL Injection vulnerabilities in SOAP. An experimental evaluation comparing this prototype with three commercial penetration testers was conducted. Results show that our prototype is able to achieve much higher detection coverage than those testers while avoiding false positives, indicating that the proposed approach can be used in real development scenarios.