nils-herrmann/clean_scirepeval huggingface dataset:
Intelligent machinery a heretical theory ; reprinted in ( Copeland 2004 ) Universal Turing Machine

Recent advances in semantic epistemolo-gies and flexible symmetries offer a viable alternative to the lookaside buffer. Here, we verify the analysis of systems. Though such a hypothesis is never an appropriate purpose, it mostly conflicts with the need to provide model checking to scholars. We show that though link-level acknowledge-99] can be made electronic, game-theoretic, and virtual, model checking and architecture can agree to solve this question.
The Effect of Behavioral Realism and Form Realism of Real-Time Avatar Faces on Verbal Disclosure, Nonverbal Disclosure, Emotion Recognition, and Copresence in Dyadic Interaction

The realism of avatars in terms of behavior and form is critical to the development of collaborative virtual environments. In the study we utilized state of the art, real-time face tracking technology to track and render facial expressions unobtrusively in a desktop CVE. Participants in dyads interacted with each other via either a video-conference (high behavioral realism and high form realism), voice only (low behavioral realism and low form realism), or an emotibox that rendered the dimensions of facial expressions abstractly in terms of color, shape, and orientation on a rectangular polygon (high behavioral realism and low form realism). Verbal and non-verbal self-disclosure were lowest in the videoconference condition while self-reported copresence and success of transmission and identification of emotions were lowest in the emotibox condition. Previous work demonstrates that avatar realism increases copresence while decreasing self-disclosure. We discuss the possibility of a hybrid realism solution that maintains high copresence without lowering self-disclosure, and the benefits of such an avatar on applications such as distance learning and therapy.
A Proposal of Metrics for Botnet Detection Based on Its Cooperative Behavior

In this paper, we propose three metrics for detecting botnets through analyzing their behavior. Our social infrastructure (i.e., the Internet) is currently experiencing the danger of bots' malicious activities as the scale of botnets increases. Although it is imperative to detect botnet to help protect computers from attacks, effective metrics for botnet detection have not been adequately researched. In this work we measure enormous amounts of traffic passing through the Asian Internet Interconnection Initiatives (AIII) infrastructure. To validate the effectiveness of our proposed metrics, we analyze measured traffic in three experiments. The experimental results reveal that our metrics are applicable for detecting botnets, but further research is needed to refine their performance
Gate drive for high speed, high power IGBTs

A new gate drive for high-voltage, high-power IGBT has been developed for the SLAC NLC (Next Linear Collider) Solid State Induction Modulator. This paper describes the design and implementation of a driver that allows an IGBT module rated at 800 A/3300 V to switch up to 3000 A at 2200 V in 3 /spl mu/s with a rate of current rise of more than 10000 A//spl mu/s, while still being short circuit protected. Issues regarding fast turn on, high de-saturation voltage detection, and low short circuit peak current are presented. A novel approach is also used to counter the effect of unequal current sharing between parallel chips inside most high-power IGBT modules. It effectively reduces the collector-emitter peak currents and thus protects the IGBT from being destroyed during soft short circuit conditions at high di/dt.
Governing Big Data: Principles and practices

As data-intensive decision making is being increasingly adopted by businesses, governments, and other agencies around the world, most organizations encountering a very large amount and variety of data are still contemplating and assessing their readiness to embrace BBig Data.[While these organizations devise various ways to deal with the challenges such data brings, the impact and importance of Big Data to information quality and governance programs should not be underestimated. Drawing upon implementation experiences of early adopters of Big Data technologies across multiple industries, this paper explores the issues and challenges involved in the management of Big Data, highlighting the principles and best practices for effective Big Data governance.
The Not-so-Staggering Effect of Staggered Animated Transitions on Visual Tracking

Interactive visual applications often rely on animation to transition from one display state to another. There are multiple animation techniques to choose from, and it is not always clear which should produce the best visual correspondences between display elements. One major factor is whether the animation relies on staggering-an incremental delay in start times across the moving elements. It has been suggested that staggering may reduce occlusion, while also reducing display complexity and producing less overwhelming animations, though no empirical evidence has demonstrated these advantages. Work in perceptual psychology does show that reducing occlusion, and reducing inter-object proximity (crowding) more generally, improves performance in multiple object tracking. We ran simulations confirming that staggering can in some cases reduce crowding in animated transitions involving dot clouds (as found in, e.g., animated 2D scatterplots). We empirically evaluated the effect of two staggering techniques on tracking tasks, focusing on cases that should most favour staggering. We found that introducing staggering has a negligible, or even negative, impact on multiple object tracking performance. The potential benefits of staggering may be outweighed by strong costs: a loss of common-motion grouping information about which objects travel in similar paths, and less predictability about when any specific object would begin to move. Staggering may be beneficial in some conditions, but they have yet to be demonstrated. The present results are a significant step toward a better understanding of animation pacing, and provide direction for further research.
A Capacitively Coupling Multifeed Slot Antenna for Metallic RFID Tag Design

This letter describes a capacitively coupling multifeed slot antenna, which is comprises a slot radiator and a small dipole coupling source for metallic plate radio frequency identification (RFID) tag design. The features of the design is that the tag can function effectively with three different RFID chips, whose impedances vary widely, and the design of the conjugate impedance match of the RFID tag can be easily achieved. The important advantages of the design are that the tag does not have to be redesigned to work with different RFID chips, and additionally, the indirect coupling mechanism helps solve the mounting issue encountered in the process of mass production of the RFID tag. The experimental tests carried out with tags in which the TI, Alien-H2, and Alien-H3 chips were incorporated show that the maximum read ranges we can achieve are 1.2, 2.8, and 4.2 m, respectively, which has been tested for an RFID reader with only 0.5 W of EIRP. The letter shows that the simulated and measured results are shown to be in good agreement with each other.
Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems

Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other.
The Social World of Twitter: Topics, Geography, and Emotions

Debate is open as to whether social media communities resemble real-life communities, and to what extent. We contribute to this discussion by testing whether established sociological theories of real-life networks hold in Twitter. In particular, for 228,359 Twitter profiles, we compute network metrics (e.g., reciprocity, structural holes, simmelian ties) that the sociological literature has found to be related to parts of one’s social world (i.e., to topics, geography and emotions), and test whether these real-life associations still hold in Twitter. We find that, much like individuals in real-life communities, social brokers (those who span structural holes) are opinion leaders who tweet about diverse topics, have geographically wide networks, and express not only positive but also negative emotions. Furthermore, Twitter users who express positive (negative) emotions cluster together, to the extent of having a correlation coefficient between one’s emotions and those of friends as high as 0.45. Understanding Twitter’s social dynamics does not only have theoretical implications for studies of social networks but also has practical implications, including the design of self-reflecting user interfaces that make people aware of their emotions, spam detection tools, and effective marketing campaigns.
Visual search has no memory

Humans spend a lot of time searching for things, such as roadside traffic signs, soccer balls or tumours in mammograms. These tasks involve the deployment of attention from one item in the visual field to the next. Common sense suggests that rejected items should be noted in some fashion so that effort is not expended in re-examining items that have been attended to and rejected. However, common sense is wrong. Here we asked human observers to search for a letter ‘T’ among letters ‘L’. This search demandsvisual attention and normally proceeds at a rate of 20–30 milliseconds per item. In the critical condition, we randomly relocated all letters every 111 milliseconds. This made it impossible for the subjects to keep track of the progress of the search. Nevertheless, the efficiency of the search was unchanged. Theories of visual search all assume that search relies on accumulating information about the identity of objects over time. Such theories predict that search efficiency will be drastically reduced if the scene is continually shuffled while the observer is trying to search through it. As we show that efficiency is not impaired, the standard theories must be revised.
Intersubjectivity and Sentiment: From Language to Knowledge

Intersubjectivity is an important concept in psychology and sociology. It refers to sharing conceptualizations through social interactions in a community and using such shared conceptualization as a resource to interpret things that happen in everyday life. In this work, we make use of intersubjectivity as the basis to model shared stance and subjectivity for sentiment analysis. We construct an intersubjectivity network which links review writers, terms they used, as well as the polarities of the terms. Based on this network model, we propose a method to learn writer embeddings which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the IMDB, Yelp 2013 and Yelp 2014 datasets show that the proposed approach has achieved the state-of-the-art performance.
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nanoscale microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Recently, deep learning has drawn much attention in computer vision because of its exceptional performance in image classification tasks. For this reason, its application to connectomic analyses holds great promise, as well. In this paper, we introduce a novel deep neural network architecture, FusionNet, for the automatic segmentation of neuronal structures in connectomics data. FusionNet leverages the latest advances in machine learning, such as semantic segmentation and residual neural networks, with the novel introduction of summation-based skip connections to allow a much deeper network architecture for a more accurate segmentation. We demonstrate the performance of the proposed method by comparing it with state-of-the-art electron microscopy (EM) segmentation methods from the ISBI EM segmentation challenge. We also show the segmentation results on two different tasks including cell membrane and cell body segmentation and a statistical analysis of cell morphology.
TwoStep: An Authentication Method Combining Text and Graphical Passwords

Text-based passwords alone are subject to dictionary attac ks s users tend to choose weak passwords in favor of memorability, as we ll as phishing attacks. Many recognition-based graphical password schemes alone, in order to offer sufficient security, require a number of rounds of veri fication, introducing usability issues. We suggest a hybrid user authentication a pproach combining text passwords, recognition-based graphical passwords, a nd a two-step process, to provide increased security with fewer rounds than such gr aphical passwords alone. A variation of this two-step authentication method, which we have implemented and deployed, is in use in the real world.
Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents

Product fit uncertainty (defined as the degree to which a consumer cannot assess whether a product’s attributes match her preferences) is proposed to be a major impediment to online markets with costly product returns and lack of consumer satisfaction. We conceptualize the nature of product fit uncertainty as an information problem and theorize its distinct effect on product returns and consumer satisfaction (versus product quality uncertainty), particularly for experience (versus search) goods without product familiarity. To reduce product fit uncertainty, we propose two Internet-enabled systems – website media (visualization systems) and online product forums (collaborative shopping systems) – that are hypothesized to attenuate the negative effect of product type (experience versus search goods) on product fit uncertainty. Hypotheses that link experience goods to product returns through the mediating role of product fit uncertainty are tested with analyses of a unique dataset composed of secondary data matched with primary direct data from numerous consumers who had recently participated in buy-it-now auctions. The results show the distinction between product fit uncertainty and quality uncertainty as two distinct dimensions of product uncertainty, and interestingly show that, relative to product quality uncertainty, product fit uncertainty has a significantly stronger effect on product returns. Notably, while product quality uncertainty is mainly driven by the experience attributes of a product, product fit uncertainty is mainly driven by both experience attributes and lack of product familiarity. The results also suggest that Internet-enabled systems are differentially used to reduce product (fit and quality) uncertainty. Notably, the use of online product forums is shown to moderate the effect of experience goods on product fit uncertainty, while website media are shown to attenuate the effect of experience goods on product quality uncertainty. The results are robust to econometric specifications and estimation methods. The paper concludes by stressing the importance of reducing the increasingly-prevalent information problem of product fit uncertainty in online markets with the aid of Internet-enabled systems.
New Adjustable Slot Meander Patch Antenna for 4G Handheld Devices

Two different antennas constructed using a new concept, the slot meander patch (SMP) design, are presented in this study. SMP antennas are designed for fourth-generation long-term evolution (4G LTE) handheld devices. These antennas are used for different target specifications: LTE-Time Division Duplex and LTE-Frequency Division Duplex (LTE TDD and LTE FDD). The first antenna is designed to operate in a wideband of 1.68-3.88 GHz to cover eight LTE TDD application frequency bands. Investigations have shown that the antenna designed with unequal meander widths has a higher efficiency compared to its equivalent antenna design with equal meander widths. The second antenna was configured as a multiband SMP antenna, which operates at three distinct frequency bands (0.5-0.75, 1.1-2.7, and 3.3-3.9 GHz), to cover eight LTE FDD application bands including the lowest and the highest bands. There is a good agreement between the measurement and simulation results for both antennas. Moreover, parametric studies have been carried out to investigate the flexible multiband antenna. Results have shown that the bandwidths can be improved through adjusting the meander widths without changing the SMP length and all other parameters.
A Literature Survey and Comprehensive Study of Intrusion Detection

With the rapid expansion of computer usage and computer network the security of the computer system has became very important. Every day new kind of attacks are being faced by industries. As the threat becomes a serious matter year by year, intrusion detection technologies are indispensable for network and computer security. A variety of intrusion detection approaches be present to resolve this severe issue but the main problem is performance. It is important to increase the detection rates and reduce false alarm rates in the area of intrusion detection. In order to detect the intrusion, various approaches have been developed and proposed over the last decade. In this paper, a detailed survey of intrusion detection based various techniques has been presented. Here, the techniques are classified as follows: i) papers related to Neural network ii) papers related to Support vector machine iii) papers related to K-means classifier iv) papers related to hybrid technique and v) paper related to other detection techniques. For comprehensive analysis, detection rate, time and false alarm rate from various research papers have been taken.
Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits.

Epidermal thickness and its relationship to age, gender, skin type, pigmentation, blood content, smoking habits and body site is important in dermatologic research and was investigated in this study. Biopsies from three different body sites of 71 human volunteers were obtained, and thickness of the stratum corneum and cellular epidermis was measured microscopically using a preparation technique preventing tissue damage. Multiple regressions analysis was used to evaluate the effect of the various factors independently of each other. Mean (SD) thickness of the stratum corneum was 18.3 (4.9) microm at the dorsal aspect of the forearm, 11.0 (2.2) microm at the shoulder and 14.9 (3.4) microm at the buttock. Corresponding values for the cellular epidermis were 56.6 (11.5) microm, 70.3 (13.6) microm and 81.5 (15.7) microm, respectively. Body site largely explains the variation in epidermal thickness, but also a significant individual variation was observed. Thickness of the stratum corneum correlated positively to pigmentation (p = 0.0008) and negatively to the number of years of smoking (p < 0.0001). Thickness of the cellular epidermis correlated positively to blood content (P = 0.028) and was greater in males than in females (P < 0.0001). Epidermal thickness was not correlated to age or skin type.
Semi-Supervised Domain Adaptation with Non-Parametric Copulas

A new framework based on the theory of copulas is proposed to address semisupervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate copula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Importantly, we introduce a novel vine copula model, which allows for this factorization in a non-parametric manner. Experimental results on regression problems with real-world data illustrate the efficacy of the proposed approach when compared to state-of-the-art techniques.
A Novel CMOS Full Adder

This paper proposes a high-speed adder cell using a new design style called "bridge". The bridge design style offers more regularity and higher density than conventional CMOS design style, by using some transistors, named bridge transistors. Results show 4.4% (@ Vdd=3 volt) to 34.1% (@ Vdd=1 volt) improvement in speed over conventional CMOS adder. HSPICE is the circuit simulator used, and the technology being used for simulations is BSIM3v3 0.18mum technology
Approximate Dynamic Programming for Ambulance Redeployment

W present an approximate dynamic programming approach for making ambulance redeployment decisions in an emergency medical service system. The primary decision is where we should redeploy idle ambulances so as to maximize the number of calls reached within a delay threshold. We begin by formulating this problem as a dynamic program. To deal with the high-dimensional and uncountable state space in the dynamic program, we construct approximations to the value function that are parameterized by a small number of parameters. We tune the parameters using simulated cost trajectories of the system. Computational experiments demonstrate the performance of the approach on emergency medical service systems in two metropolitan areas. We report practically significant improvements in performance relative to benchmark static policies.
Online Adaptive Hidden Markov Model for Multi-Tracker Fusion

In this paper, we propose a novel method for visual object tracking called HMMTxD. The method fuses observations from complementary out-of-the box trackers and a detector by utilizing a hidden Markov model whose latent states correspond to a binary vector expressing the failure of individual trackers. The Markov model is trained in an unsupervised way, relying on an online learned detector to provide a source of tracker-independent information for a modified BaumWelch algorithm that updates the model w.r.t. the partially annotated data. We show the effectiveness of the proposed method on combination of two and three tracking algorithms. The performance of HMMTxD is evaluated on two standard benchmarks (CVPR2013 and VOT) and on a rich collection of 77 publicly available sequences. The HMMTxD outperforms the state-of-the-art, often significantly, on all datasets in almost all criteria.
1 Density-based clustering algorithms – DBSCAN and SNN

This document describes the implementation of two density-based clustering algorithms: DBSCAN [Ester1996] and SNN [Ertoz2003]. These algorithms were implemented within the context of the LOCAL project [Local2005] as part of a task that aims to create models of the geographic space (Space Models) to be used in context-aware mobile systems. Here, the role of the clustering algorithms is to identify clusters of Points of Interest (POIs) and then use the clusters to automatically characterize geographic regions.