nils-herrmann/clean_scirepeval huggingface dataset:
Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning

The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.
Multiband 10-Antenna Array for Sub-6 GHz MIMO Applications in 5-G Smartphones

A multi-band 10-antenna array working at the sub-6-GHz spectrum (LTE bands 42/43 and LTE band 46) for massive multiple-input multiple-output (MIMO) applications in future 5G smartphones is proposed. To realize <inline-formula> <tex-math notation="LaTeX">$10\times 10$ </tex-math></inline-formula> MIMO applications in three LTE bands, 10 T-shaped coupled-fed slot antenna elements that can excite dual resonant modes are integrated into a system circuit board. Spatial and polarization diversity techniques are implemented on these elements so that the improved isolation and mitigated coupling effects can be achieved. The proposed antenna array was manufactured and experimentally measured. Desirable antenna efficiencies of higher than 42% and 62% were measured in the low band and high band, respectively. Vital results, such as the envelope correlation coefficient, channel capacity, and mean effective gain ratio, have also been computed and analyzed. The calculated ergodic channel capacities of the <inline-formula> <tex-math notation="LaTeX">$10\times 10$ </tex-math></inline-formula> MIMO system working in the LTE bands 42/43 and LTE band 46 reached up to 48 and 51.4 b/s/Hz, respectively.
Synthesizing open worlds with constraints using locally annealed reversible jump MCMC

We present a novel Markov chain Monte Carlo (MCMC) algorithm that generates samples from transdimensional distributions encoding complex constraints. We use factor graphs, a type of graphical model, to encode constraints as factors. Our proposed MCMC method, called locally annealed reversible jump MCMC, exploits knowledge of how dimension changes affect the structure of the factor graph. We employ a sequence of annealed distributions during the sampling process, allowing us to explore the state space across different dimensionalities more freely. This approach is motivated by the application of layout synthesis where relationships between objects are characterized as constraints. In particular, our method addresses the challenge of synthesizing open world layouts where the number of objects are not fixed and optimal configurations for different numbers of objects may be drastically different. We demonstrate the applicability of our approach on two open world layout synthesis problems: coffee shops and golf courses.
Foreground Clustering for Joint Segmentation and Localization in Videos and Images

This paper presents a novel framework in which video/image segmentation and localization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner. The proposed framework leverages two representations at different levels, exploits the spatial relationship between bounding boxes and superpixels as linear constraints and simultaneously discriminates between foreground and background at bounding box and superpixel level. Different from previous approaches that mainly rely on discriminative clustering, we incorporate a foreground model that minimizes the histogram difference of an object across all image frames. Exploiting the geometric relation between the superpixels and bounding boxes enables the transfer of segmentation cues to improve localization output and vice-versa. Inclusion of the foreground model generalizes our discriminative framework to video data where the background tends to be similar and thus, not discriminative. We demonstrate the effectiveness of our unified framework on the YouTube Object video dataset, Internet Object Discovery dataset and Pascal VOC 2007.
The insider threat to information systems and the effectiveness of ISO17799

Insider threat is widely recognised as an issue of utmost importance for IS security management. In this paper, we investigate the approach followed by ISO17799, the dominant standard in IS security management, in addressing this type of threat. We unfold the criminology theory that has designated the measures against insider misuse suggested by the standard, i.e. the General Deterrence Theory, and explore the possible enhancements to the standard that could result from the study of more recent criminology theories. The paper concludes with supporting the argument for a multiparadigm and multidisciplinary approach towards IS security management and insider threat mitigation.
A model of customer relationship management and business intelligence systems for catalogue and online retailers

As more retailers evolve into customer-centric and segment-based business, business intelligence (BI) and customer relationship management (CRM) systems are playing a key role in achieving and maintaining competitive advantage. For the past ten years, the authors have had the rare opportunity of observing and interviewing employees and managers of three different management teams at three separate Fingerhut companies as they experimented with various ITs for their companies. When the first Fingerhut company peaked in 1998, as many as 200 analysts and 40 statisticians mined the database for insights that helped predict consumer shopping patterns and credit behaviour. Data mining and BI helped Fingerhut spot shopping patterns, bring product offerings to the right customers, and nurture customer relationships. By 1998, Fingerhut was the second largest catalogue retailer in the U.S. with revenues nearing $2 billion. However, after Federated acquired Fingerhut in 1999 and made it a subsidiary, Fingerhut Net, it suffered great losses and was eventually liquidated. Finally, a new company, Fingerhut Direct Marketing, was resurrected in 2002 under a new management team, and it once again became successful. What went right? What went wrong? The paper concludes with CRM and BI systems success factors and a discussion of lessons learned.
PCC: Re-architecting Congestion Control for Consistent High Performance

TCP and its variants have suffered from surprisingly poor performance for decades. We argue the TCP family has little hope to achieve consistent high performance due to a fundamental architectural deficiency: hardwiring packet-level events to control responses without understanding the real performance result of its actions. We propose Performance-oriented Congestion Control (PCC), a new congestion control architecture in which each sender continuously observes the connection between its actions and empirically experienced performance, enabling it to consistently adopt actions that result in high performance. We prove that PCC converges to a stable and fair equilibrium. Across many real-world and challenging environments, PCC shows consistent and often 10× performance improvement, with better fairness and stability than TCP. PCC requires no router hardware support or new packet format.
0.35μm, 30V fully isolated and low-Ron nLDMOS for DC-DC applications

In this paper, we present a new approach to integrate in a 0.35 μm BCD technology, low Ron LDMOS power transistors with highly competitive Specific Resistance figure of merit (Rsp, defined as Ron*Area). The LDMOS are fully isolated in order to support applications which may bias the source/drain electrodes below the substrate potential, which is critical for devices used in high-current, high-frequency switching applications. The new devices are suitable for high-efficiency DC-DC converter products with operating voltage of up to 30V, such as mobile PMICs. For maximum performance, two different extended-drain LDMOS structures have been developed to cover the entire operating voltage range: for 16V and below, a planar-gate structure is used and for 20V and above, a non-planar “offset-LOCOS” gate is used for 20V and above.
Design of Smart Traffic Light Controller Using Embedded System

The occurrence of traffic congestion at the intersections in cities is one of major problem. The present traffic light controlling system could not sufficient to tackle the traffic congestion due to the lack of efficiency in the designing. The main design problems of present traffic light controller system are: A. Fixed Time Interval: Vehicles at the traffic lane wait until the green light on. Due to the fixed time interval of red & green light, suppose a road is always crowded with vehicles and go-ahead time is short. So, vehicles can’t pass through the road in the time allowed. But sub lane has few vehicles and go-ahead time is relatively long. The proposed system provides dynamic time interval for traffic lights according to length of vehicles present at each lane. B. No mechanism provided for the occurrence of an emergency vehicle: The emergency vehicle such as ambulance, fire brigade and police will be stuck especially at the traffic light junction. To solve these problems, we have proposed the system called as “Smart Traffic Light Controlling System”. The proposed system handles the occurrence of emergency vehicle. It makes all the signals red except one from the emergency vehicle passes. C. No Traffic information to user: The proposed system provides GSM phone interface to the user, with SMS facility to those who wish to obtain the latest traffic information on congested roads. The map of the traffic signal will be provided to the users on their request. The proposed system has been developed using the sensor assembly along with embedded technology.
Mesolimbic functional magnetic resonance imaging activations during reward anticipation correlate with reward-related ventral striatal dopamine release.

The dopaminergic mechanisms that control reward-motivated behavior are the subject of intense study, but it is yet unclear how, in humans, neural activity in mesolimbic reward-circuitry and its functional neuroimaging correlates are related to dopamine release. To address this question, we obtained functional magnetic resonance imaging (fMRI) measures of reward-related neural activity and [(11)C]raclopride positron emission tomography measures of dopamine release in the same human participants, while they performed a delayed monetary incentive task. Across the cohort, a positive correlation emerged between neural activity of the substantia nigra/ventral tegmental area (SN/VTA), the main origin of dopaminergic neurotransmission, during reward anticipation and reward-related [(11)C]raclopride displacement as an index of dopamine release in the ventral striatum, major target of SN/VTA dopamine neurons. Neural activity in the ventral striatum/nucleus accumbens itself also correlated with ventral striatal dopamine release. Additionally, high-reward-related dopamine release was associated with increased activation of limbic structures, such as the amygdala and the hippocampus. The observed correlations of reward-related mesolimbic fMRI activation and dopamine release provide evidence that dopaminergic neurotransmission plays a quantitative role in human mesolimbic reward processing. Moreover, the combined neurochemical and hemodynamic imaging approach used here opens up new perspectives for the investigation of molecular mechanisms underlying human cognition.
Translating into Morphologically Rich Languages with Synthetic Phrases

Translation into morphologically rich languages is an important but recalcitrant problem in MT. We present a simple and effective approach that deals with the problem in two phases. First, a discriminative model is learned to predict inflections of target words from rich source-side annotations. Then, this model is used to create additional sentencespecific wordand phrase-level translations that are added to a standard translation model as “synthetic” phrases. Our approach relies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can successfully be used in place of a supervised analyzer. We report significant improvements in translation quality when translating from English to Russian, Hebrew and Swahili.
Neural Darwinism and consciousness

Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.
Phase retrieval algorithms: a comparison.

Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods. Both the problem of phase retrieval from two intensity measurements (in electron microscopy or wave front sensing) and the problem of phase retrieval from a single intensity measurement plus a non-negativity constraint (in astronomy) are considered, with emphasis on the latter. It is shown that both the error-reduction algorithm for the problem of a single intensity measurement and the Gerchberg-Saxton algorithm for the problem of two intensity measurements converge. The error-reduction algorithm is also shown to be closely related to the steepest-descent method. Other algorithms, including the input-output algorithm and the conjugate-gradient method, are shown to converge in practice much faster than the error-reduction algorithm. Examples are shown.
Transparent authentication systems for mobile device security: A review

Sensitive data such as text messages, contact lists, and personal information are stored on mobile devices. This makes authentication of paramount importance. More security is needed on mobile devices since, after point-of-entry authentication, the user can perform almost all tasks without having to re-authenticate. For this reason, many authentication methods have been suggested to improve the security of mobile devices in a transparent and continuous manner, providing a basis for convenient and secure user re-authentication. This paper presents a comprehensive analysis and literature review on transparent authentication systems for mobile device security. This review indicates a need to investigate when to authenticate the mobile user by focusing on the sensitivity level of the application, and understanding whether a certain application may require a protection or not.
Adaptive Intrusion Detection Using Machine Learning

This paper applies PCA for feature selection with Naïve Bayes for classification in order to build a network intrusion detection system. For experimental analysis, KDDCup 1999 intrusion detection benchmark dataset have been used. The 2 class classification is performed. The experimental results show that the proposed approach is very accurate with low false positive rate and takes less time in comparison to other existing approaches while building an efficient network intrusion detection system.
Real-Time Compressive Tracking

It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, these mis-aligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis. Our appearance model employs nonadaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is adopted to efficiently extract the features for the appearance model. We compress samples of foreground targets and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.
A 3.9-ps RMS Precision Time-to-Digital Converter Using Ones-Counter Encoding Scheme in a Kintex-7 FPGA

A 3.9-ps time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter (TDC) is implemented in a Xilinx Kintex-7 field programmable gate array (FPGA). Unlike previous work, the TDC is achieved with a multichain tapped-delay line (TDL) followed by an ones-counter encoder. The four normal TDLs merged together make the TDC bins very small, so that the time precision can be significantly improved. The ones-counter encoder naturally applies global bubble error correction to the output of TDL, thus the TDC design is relatively simple even when using FPGAs made with current advanced process technology. The TDC implementation is a generally applicable method that can simultaneously achieve high time precision and high measurement throughput.
HOW CIO POSITION INFLUENCES INFORMATION TECHNOLOGY INVESTMENTS

Despite the important roles chief information officers (CIOs) play in developing information technology (IT) strategies and carrying out IT plans, prior research has not examined the impact of having a formal CIO position on IT investments and firm performance. Drawing on the upper echelons theory and using data from over 250 US firms over the 1999-2003 period, we address how a firm’s CIO position influences the level of the firm’s IT investments and the returns to IT investments. We find that CIO position in organizational hierarchy has a significant impact on IT investments: firms with a formal CIO position make higher levels of IT investments, compared to firms without a formal CIO position. We also find that returns to IT investments are greater for firms whose CIOs possess additional titles and/or expanded responsibilities, compared to firms without CIOs or CIOs in less senior management positions. In addition, examining the role of industry characteristics, we find that the impact of CIO’s position on IT investments is much greater in “transform” industries compared to “automate” and “informate” industries. In transform industries, alignment of strategic IT roles leads to improved financial performance. Further, we find that CIO position has significantly stronger impacts in IT intensive firms than in non-IT intensive firms, in terms of both its impact on IT investments and the moderating effect on returns to IT
Toward a unified theory of the multitasking continuum: from concurrent performance to task switching, interruption, and resumption

Multitasking in user behavior can be represented along a continuum in terms of the time spent on one task before switching to another. In this paper, we present a theory of behavior along the multitasking continuum, from concurrent tasks with rapid switching to sequential tasks with longer time between switching. Our theory unifies several theoretical effects - the ACT-R cognitive architecture, the threaded cognition theory of concurrent multitasking, and the memory-for-goals theory of interruption and resumption - to better understand and predict multitasking behavior. We outline the theory and discuss how it accounts for numerous phenomena in the recent empirical literature.
An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions

This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations.
Reflections on the generalized bas-relief ambiguity

Prior work has argued that when a Lambertian surface in fixed pose is observed in multiple images under varying distant illumination, there is an equivalence class of surfaces given by the generalized bas-relief (GBR) ambiguity that could have produced these images. In contrast, this paper shows that for general nonconvex surfaces, interreflections completely resolve the GBR ambiguity. In turn, the full Euclidean geometry can be recovered from uncalibrated photometric stereo for which the light source directions and strengths are unknown. Further, we show that surfaces with a translational symmetry do not lend enough constraints to be disambiguated by inter reflections.