nils-herrmann/clean_scirepeval huggingface dataset:
A Real-Time Distributed Light Field Camera

We present the design and implementation of a real-time, distributed light field camera. Our system allows multiple viewers to navigate virtual cameras in a dynamically changing light field that is captured in real-time. Our light field camera consists of 64 commodity video cameras that are connected to off-the-shelf computers. We employ a distributed rendering algorithm that allows us to overcome the data bandwidth problems inherent in dynamic light fields. Our algorithm works by selectively transmitting only those portions of the video streams that contribute to the desired virtual views. This technique not only reduces the total bandwidth, but it also allows us to scale the number of cameras in our system without increasing network bandwidth. We demonstrate our system with a number of examples.
Morphable Displacement Field Based Image Matching for Face Recognition across Pose

Fully automatic Face Recognition Across Pose (FRAP) is one of the most desirable techniques, however, also one of the most challenging tasks in face recognition field. Matching a pair of face images in different poses can be converted into matching their pixels corresponding to the same semantic facial point. Following this idea, given two images G and P in different poses, we propose a novel method, named Morphable Displacement Field (MDF), to match G with P ’s virtual view under G’s pose. By formulating MDF as a convex combination of a number of template displacement fields generated from a 3D face database, our model satisfies both global conformity and local consistency. We further present an approximate but effective solution of the proposed MDF model, named implicit Morphable Displacement Field (iMDF), which synthesizes virtual view implicitly via an MDF by minimizing matching residual. This formulation not only avoids intractable optimization of the high-dimensional displacement field but also facilitates a constrained quadratic optimization. The proposed method can work well even when only 2 facial landmarks are labeled, which makes it especially suitable for fully automatic FRAP system. Extensive evaluations on FERET, PIE and Multi-PIE databases show considerable improvement over state-ofthe-art FRAP algorithms in both semi-automatic and fully automatic evaluation protocols.
A 32nm fully integrated reconfigurable switched-capacitor DC-DC converter delivering 0.55W/mm2 at 81% efficiency

With the rising integration levels used to increase digital processing performance, there is a clear need for multiple independent on-chip supplies in order to support per-IP or block power management. Simply adding multiple off-chip DCDC converters is not only difficult due to supply impedance concerns, but also adds cost to the platform by increasing motherboard size and package complexity. There is therefore a strong motivation to integrate voltage conversion blocks on the silicon chip.
Likelihood to abort an online transaction: influences from cognitive evaluations, attitudes, and behavioral variables

This study identifies evaluative, attitudinal, and behavioral factors that enhance or reduce the likelihood of consumers aborting intended online transactions (transaction abort likelihood). Path analyses show that risk perceptions associated with eshopping have direct influence on the transaction abort likelihood, whereas benefit perceptions do not. In addition, consumers who have favorable attitudes toward e-shopping, purchasing experiences from the Internet, and high purchasing frequencies from catalogs are less likely to abort intended transactions. The results also show that attitude toward e-shopping mediate relationships between the transaction abort likelihood and other predictors (i.e., effort saving, product offering, control in the information search, and time spent on the Internet per visit).
Generalized second price auction with probabilistic broad match

Generalized Second Price (GSP) auctions are widely used by search engines today to sell their ad slots. Most search engines have supported the broad match between queries and bid keywords when executing the GSP auctions, however, it has been revealed that the GSP auction with the standard broad-match mechanism they are currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g., its theoretical properties are known only for the single-slot case and full-information setting, and even in this simple setting, the corresponding worst-case social welfare can be rather bad). To address this issue, we propose a novel broad-match mechanism, which we call the Probabilistic Broad-Match (PBM) mechanism. Different from SBM that puts together the ads bidding on all the keywords matched to a given query for the GSP auction, the GSP with PBM (denoted as PBM-GSP) randomly samples a keyword according to a predefined probability distribution and only runs the GSP auction for the ads bidding on this sampled keyword. We perform a comprehensive study on the theoretical properties of the PBM-GSP. Specifically, we study its social welfare in the worst equilibrium, in both full-information and Bayesian settings. The results show that PBM-GSP can generate larger welfare than SBM-GSP} under mild conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in Bayesian setting. To the best of our knowledge, this is the first work on broad-match mechanisms for GSP that goes beyond the single-slot case and the full-information setting.
Scalable Probabilistic Databases with Factor Graphs and MCMC

Incorporating probabilities into the semantics of incomplete databases has posed many challenges, forcing systems to sacrifice modeling power, scalability, or treatment of relational algebra operators. We propose an alternative approach where the underlying relational database always represents a single world, and an external factor graph encodes a distribution over possible worlds; Markov chain Monte Carlo (MCMC) inference is then used to recover this uncertainty to a desired level of fidelity. Our approach allows the efficient evaluation of arbitrary queries over probabilistic databases with arbitrary dependencies expressed by graphical models with structure that changes during inference. MCMC sampling provides efficiency by hypothesizing modifications to possible worlds rather than generating entire worlds from scratch. Queries are then run over the portions of the world that change, avoiding the onerous cost of running full queries over each sampled world. A significant innovation of this work is the connection between MCMC sampling and materialized view maintenance techniques: we find empirically that using view maintenance techniques is several orders of magnitude faster than naively querying each sampled world. We also demonstrate our system’s ability to answer relational queries with aggregation, and demonstrate additional scalability through the use of parallelization on a real-world complex model of information extraction. This framework is sufficiently expressive to support probabilistic inference not only for answering queries, but also for inferring missing database content from raw evidence.
Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation

We consider the problem of learning deep neural networks (DNNs) for object category segmentation, where the goal is to label each pixel in an image as being part of a given object (foreground) or not (background). Deep neural networks are usually trained with simple loss functions (e.g., softmax loss). These loss functions are appropriate for standard classification problems where the performance is measured by the overall classification accuracy. For object category segmentation, the two classes (foreground and background) are very imbalanced. The intersectionover-union (IoU) is usually used to measure the performance of any object category segmentation method. In this paper, we propose an approach for directly optimizing this IoU measure in deep neural networks. Our experimental results on two object category segmentation datasets demonstrate that our approach outperforms DNNs trained with standard softmax loss.
The use of NARX neural networks to predict chaotic time series

The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural networks (RNN) models are not only important for the forecasting of time series but also generally for the control of the dynamical system. A RNN with a sufficiently large number of neurons is a nonlinear autoregressive and moving average (NARMA) model, with “moving average” referring to the inputs. The prediction can be assimilated to identification of dynamic process. An architectural approach of RNN with embedded memory, “Nonlinear Autoregressive model process with eXogenous input” (NARX), showing promising qualities for dynamic system applications, is analyzed in this paper. The performances of the NARX model are verified for several types of chaotic or fractal time series applied as input for neural network, in relation with the number of neurons, the training algorithms and the dimensions of his embedded memory. In addition, this work has attempted to identify a way to use the classic statistical methodologies (R/S Rescaled Range analysis and Hurst exponent) to obtain new methods of improving the process efficiency of the prediction chaotic time series with NARX.  Chaotic Time Series, Hurst Exponent, Prediction, Recurrent Neural Networks, NARX Model
Twitter Emotion Analysis in Earthquake Situations

Emotion keyword spotting approach can detect emotion well for explicit emotional contents while it obviously cannot compare to supervised learning approaches for detecting emotional contents of particular events. In this paper, we target earthquake situations in Japan as the particular events for emotion analysis because the affected people often show their states and emotions towards the situations via social networking sites. Additionally, tracking crowd emotions in the Internet during the earthquakes can help authorities to quickly decide appropriate assistance policies without paying the cost as the traditional public surveys. Our three main contributions in this paper are: a) the appropriate choice of emotions; b) the novel proposal of two classification methods for determining the earthquake related tweets and automatically identifying the emotions in Twitter; c) tracking crowd emotions during different earthquake situations, a completely new application of emotion analysis research. Our main analysis results show that Twitter users show their Fear and Anxiety right after the earthquakes occurred while Calm and Unpleasantness are not showed clearly during the small earthquakes but in the large tremor.
Ultra-broadband low axial ratio corrugated quad-ridge polarizer

The design and fabrication of an Ultra broadband square quad-ridge polarizer is discussed here. The principal advantages of this topology relay on both the instantaneous bandwidth and the axial ratio improvement. Experimental measurements exhibit very good agreement with the predicted results given by Mode Matching techniques. The structure provides an extremely flat axial ratio (AR< 0.4dB) and good return losses >25dB at both square ports over the extended Ku band (= 60%). Moreover, yield analysis and scaling properties demonstrate the robustness of this design against fabrication tolerances.
Technical Debt in Test Automation

Automated test execution is one of the more popular and available strategies to minimize the cost for software testing, and is also becoming one of the central concepts in modern software development as methods such as test-driven development gain popularity. Published studies on test automation indicate that the maintenance and development of test automation tools commonly encounter problems due to unforeseen issues. To further investigate this, we performed a case study on a telecommunication subsystem to seek factors that contribute to inefficiencies in use, maintenance, and development of the automated testing performed within the scope of responsibility of a software design team. A qualitative evaluation of the findings indicates that the main areas of improvement in this case are in the fields of interaction design and general software design principles, as applied to test execution system development.
Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention

A critical function in both machine vision and biological vision systems is attentional selection of scene regions worthy of further analysis by higher-level processes such as object recognition. Here we present the first model of spatial attention that (1) can be applied to arbitrary static and dynamic image sequences with interactive tasks and (2) combines a general computational implementation of both bottom-up (BU) saliency and dynamic top-down (TD) task relevance; the claimed novelty lies in the combination of these elements and in the fully computational nature of the model. The BU component computes a saliency map from 12 low-level multi-scale visual features. The TD component computes a low-level signature of the entire image, and learns to associate different classes of signatures with the different gaze patterns recorded from human subjects performing a task of interest. We measured the ability of this model to predict the eye movements of people playing contemporary video games. We found that the TD model alone predicts where humans look about twice as well as does the BU model alone; in addition, a combined BU*TD model performs significantly better than either individual component. Qualitatively, the combined model predicts some easy-to-describe but hard-to-compute aspects of attentional selection, such as shifting attention leftward when approaching a left turn along a racing track. Thus, our study demonstrates the advantages of integrating BU factors derived from a saliency map and TD factors learned from image and task contexts in predicting where humans look while performing complex visually-guided behavior.
Modeling and input shaping control of a micro coaxial radio-controlled helicopter carrying a suspended load

Helicopters are often used to transport supplies and equipment to hard-to-reach areas. When a load is carried via suspension cables below a helicopter, the load oscillates in response to helicopter motion and external disturbances, such as wind. This oscillation is dangerous and adversely affects control of the helicopter, especially when carrying heavy loads. To provide better control over the helicopter, one approach is to suppress the load swing dynamics using a command-filtering method called input shaping. This approach does not require real-time measurement or estimation of the load states. A simple model of a helicopter carrying a suspended load is developed and experimentally verified on a micro coaxial radio-controlled helicopter. In addition, the effectiveness of input shaping at eliminating suspended load oscillation is demonstrated on the helicopter. The proposed model may assist with the design of input-shaping controllers for a wide array of helicopters carrying suspended loads.
Impact Of Employee Participation On Job Satisfaction , Employee Commitment And Employee Productivity

It is widely believed that the employee participation may affect employee’s job satisfaction; employee productivity, employee commitment and they all can create comparative advantage for the organization. The main intention of this study was to find out relationship among employee participation, job satisfaction, employee productivity and employee commitment. For the matter 34 organizations from Oil & Gas, Banking and Telecommunication sectors were contacted, of which 15 responded back. The findings of this study are that employee participation not only an important determinant of job satisfaction components. Increasing employee participation will have a positive effect on employee’s job satisfaction, employee commitment and employee productivity. Naturally increasing employee participation is a long-term process, which demands both attention from management side and initiative from the employee side.
Big data: extending the business strategy toolbox

The business world is rapidly digitizing as companies embrace sensors, mobile devices, radio frequency identification, audio and video streams, software logs, and the Internet to predict needs, avert fraud and waste, understand relationships, and connect with stakeholders both internal and external to the firm. Digitization creates challenges because for most companies it is unevenly distributed throughout the organization: in a 2013 survey, only 39% of company-wide investment in digitization was identified as being in the IT budget (Weill and Woerner, 2013a). This uneven, disconnected investment makes it difficult to consolidate and simplify the increasing amount of data that is one of the outcomes of digitization. This in turn makes it more difficult to derive insight – and then proceed based on that insight. Early big data research identified over a dozen characteristics of data (e.g., location, network associations, latency, structure, softness) that challenge extant data management practices (Santos and Singer, 2012). Constantiou and Kallinikos’ article describes how the nature of big data affects the ability to derive insight, and thus inhibits strategy creation. One of the important insights of this article is how big data challenges the premises and the time horizons of strategy making. Much of big data, while seemingly valuable, does not fit into the recording, measurement, and assessment systems that enterprises have built up to aid in enterprise decision making. And constantly modified and volatile data doesn’t easily form into stable interpretable patterns, confounding prediction. As they note, a focus on real-time data ‘undermines long-term planning, and reframes the trade-offs between short-term and long-term decisions’ (9). While Constantiou and Kallinikos describe the challenges that big data poses to strategy creation, they do not offer insights about how enterprises might ameliorate or even overcome those challenges. Big data is here to stay and every enterprise will have to accommodate the problematic nature of big data as it decides on a course of action. This commentary is an effort to show how big data is being used in practice to craft strategy and the company business model. Research at the MIT Center for Information Systems Research has found that the upsurge in digitization, and the accompanying increase in the amount of data, has prompted companies to reexamine their fundamental business models and explore opportunities to improve and innovate. In both cases, companies are not replacing their business strategy toolboxes, but rather are using existing toolboxes more effectively – they now have access to essential data needed to solve problems or gain insights that was not possible to collect before. The results are quite exciting.
Stalking online: on user privacy in social networks

With the extreme popularity of Web and online social networks, a large amount of personal information has been made available over the Internet. On the other hand, advances in information retrieval, data mining and knowledge discovery technologies have enabled users to efficiently satisfy their information needs over the Internet or from large-scale data sets. However, such technologies also help the adversaries such as web stalkers to discover private information about their victims from mass data.
 In this paper, we study privacy-sensitive information that are accessible from the Web, and how these information could be utilized to discover personal identities. In the proposed scenario, an adversary is assumed to possess a small piece of "seed" information about a targeted user, and conduct extensive and intelligent search to identify the target over both the Web and an information repository collected from the Web. In particular, two types of attackers are modeled, namely tireless attackers and resourceful attackers. We then analyze detailed attacking mechanisms that could be performed by these attackers, and quantify the threats of both types of attacks to general Web users. With extensive experiments and sophisticated analysis, we show that a large portion of users with online presence are highly identifiable, even when only a small piece of (possibly inaccurate) seed information is known to the attackers.
Context-sensitive analysis without calling-context

Since Sharir and Pnueli, algorithms for context-sensitivity have been defined in terms of ‘valid’ paths in an interprocedural flow graph. The definition of valid paths requires atomic call and ret statements, and encapsulated procedures. Thus, the resulting algorithms are not directly applicable when behavior similar to call and ret instructions may be realized using non-atomic statements, or when procedures do not have rigid boundaries, such as with programs in low level languages like assembly or RTL. We present a framework for context-sensitive analysis that requires neither atomic call and ret instructions, nor encapsulated procedures. The framework presented decouples the transfer of control semantics and the context manipulation semantics of statements. A new definition of context-sensitivity, called stack contexts, is developed. A stack context, which is defined using trace semantics, is more general than Sharir and Pnueli’s interprocedural path based calling-context. An abstract interpretation based framework is developed to reason about stack-contexts and to derive analogues of calling-context based algorithms using stack-context. The framework presented is suitable for deriving algorithms for analyzing binary programs, such as malware, that employ obfuscations with the deliberate intent of defeating automated analysis. The framework is used to create a context-sensitive version of Venable et al.’s algorithm for detecting obfuscated calls in x86 binaries. Experimental results from comparing context insensitive, Sharir and Pnueli’s callingcontext-sensitive, and stack-context-sensitive versions of the algorithm are presented.
Black-box α-divergence Minimization

Dataset BB-α=BO BB-α=1 BB-α=10−6 BB-VB Avg. α Boston -2.549±0.019 -2.621±0.041 -2.614±0.021 -2.578±0.017 0.45±0.04 Concrete -3.104±0.015 -3.126±0.018 -3.119±0.010 -3.118±0.010 0.72±0.03 Energy -0.979±0.028 -1.020±0.045 -0.945±0.012 -0.994±0.014 0.72±0.03 Wine -0.949±0.009 -0.945±0.008 -0.967±0.008 -0.964±0.007 0.86±0.04 Yacht -1.102±0.039 -2.091±0.067 -1.594±0.016 -1.646±0.017 0.48±0.01 Avg. Rank 1.835 ±0.065 2.504±0.080 2.766±0.061 2.895±0.057
Stegobot: A Covert Social Network Botnet

We propose Stegobot, a new generation botnet that communicates over probabilistically unobservable communication channels. It is designed to spread via social malware attacks and steal information from its victims. Unlike conventional botnets, Stegobot traffic does not introduce new communication endpoints between bots. Instead, it is based on a model of covert communication over a social-network overlay – bot to botmaster communication takes place along the edges of a social network. Further, bots use image steganography to hide the presence of communication within image sharing behavior of user interaction. We show that it is possible to design such a botnet even with a less than optimal routing mechanism such as restricted flooding. We analyzed a real-world dataset of image sharing between members of an online social network. Analysis of Stegobot’s network throughput indicates that stealthy as it is, it is also functionally powerful – capable of channeling fair quantities of sensitive data from its victims to the botmaster at tens of megabytes every month