nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Approach for Brain Tumor Detection Using Support Vector Machine , K-Means and PCA Algorithm

The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operator assisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use Support Vector Machine, Kmean and PCA shown great potential in this field. Principal Component Analysis gives fast and accurate tool for Feature Extraction of the tumors. Kmean is used for Image Segmentation. Support Vector Mahine with Training and Testing of Brain Tumor Images techniques is implemented for automated brain tumor classification.
A Fast Algorithm for Finding Dominators in a Flowgraph

A fast algorithm for finding dominators in a flowgraph is presented. The algorithm uses depth-first search and an efficient method of computing functions defined on paths in trees. A simple implementation of the algorithm runs in <italic>O</italic>(<italic>m</italic> log <italic>n</italic>) time, where <italic>m</italic> is the number of edges and <italic>n</italic> is the number of vertices in the problem graph. A more sophisticated implementation runs in <italic>O</italic>(<italic>m</italic>α(<italic>m</italic>, <italic>n</italic>)) time, where α(<italic>m</italic>, <italic>n</italic>) is a functional inverse of Ackermann's function.
Both versions of the algorithm were implemented in Algol W, a Stanford University version of Algol, and tested on an IBM 370/168. The programs were compared with an implementation by Purdom and Moore of a straightforward <italic>O</italic>(<italic>mn</italic>)-time algorithm, and with a bit vector algorithm described by Aho and Ullman. The fast algorithm beat the straightforward algorithm and the bit vector algorithm on all but the smallest graphs tested.
An Analog Integrated Circuit Beamformer for High-Frequency Medical Ultrasound Imaging

We designed and fabricated a dynamic receive beamformer integrated circuit (IC) in 0.35-μm CMOS technology. This beamformer IC is suitable for integration with an annular array transducer for high-frequency (30-50 MHz) intravascular ultrasound (IVUS) imaging. The beamformer IC consists of receive preamplifiers, an analog dynamic delay-and-sum beamformer, and buffers for 8 receive channels. To form an analog dynamic delay line we designed an analog delay cell based on the current-mode first-order all-pass filter topology, as the basic building block. To increase the bandwidth of the delay cell, we explored an enhancement technique on the current mirrors. This technique improved the overall bandwidth of the delay line by a factor of 6. Each delay cell consumes 2.1-mW of power and is capable of generating a tunable time delay between 1.75 ns to 2.5 ns. We successfully integrated the fabricated beamformer IC with an 8-element annular array. Experimental test results demonstrated the desired buffering, preamplification and delaying capabilities of the beamformer.
An overview on crime prediction methods

In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical perspectives. Nevertheless, to predict future crime accurately with a better performance, it is a challenging task because of the increasing numbers of crime in present days. Therefore, crime prediction method is important to identify the future crime and reduces the numbers of crime. Currently, some researchers have been conducted a study to predict crime based on particular inputs. The performance of prediction models can be evaluated using a variety of different prediction methods such as support vector machine, multivariate time series and artificial neural network. However, there are still some limitations on their findings to provide an accurate prediction for the location of crimes. A large number of research papers on this topic have already been published previously. Thus, in this paper, we thoroughly review each of them and summarized the outcomes. Our objective is to identify current implementations of crime prediction method and the possibility to enhance it for future needs.
Overview on Additive Manufacturing Technologies

This paper provides an overview on the main additive manufacturing/3D printing technologies suitable for many satellite applications and, in particular, radio-frequency components. In fact, nowadays they have become capable of producing complex net-shaped or nearly net-shaped parts in materials that can be directly used as functional parts, including polymers, metals, ceramics, and composites. These technologies represent the solution for low-volume, high-value, and highly complex parts and products.
Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network

Screening for vision threatening diabetic retinopathy by grading digital retinal images reduces the risk of blindness in people with diabetes. Computer-aided diagnosis can aid human graders to cope with this mounting problem. We propose to use a 10-layer convolutional neural network to automatically, simultaneously segment and discriminate exudates, haemorrhages and micro-aneurysms. Input image is normalized before segmentation. The net is trained in two stages to improve performance. On average, our net on 30,275,903 effective points achieved a sensitivity of 0.8758 and 0.7158 for exudates and dark lesions on the CLEOPATRA database. It also achieved a sensitivity of 0.6257 and 0.4606 for haemorrhages and micro-aneurysms. This study shows that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy.
Random Walks on Graphs: a Survey

Dedicated to the marvelous random walk of Paul Erd} os through universities, continents, and mathematics Various aspects of the theory of random walks on graphs are surveyed. In particular, estimates on the important parameters of access time, commute time, cover time and mixing time are discussed. Connections with the eigenvalues of graphs and with electrical networks, and the use of these connections in the study of random walks is described. We also sketch recent algorithmic applications of random walks, in particular to the problem of sampling. 0.  Given a graph and a starting point, we select a neighbor of it at random, and move to this neighbor; then we select a neighbor of this point at random, and move to it etc. The (random) sequence of points selected this way is a random walk on the graph. A random walk is a nite Markov chain that is time-reversible (see below). In fact, there is not much diierence between the theory of random walks on graphs and the theory of nite Markov chains; every Markov chain can be viewed as random walk on a directed graph, if we allow weighted edges. Similarly, time-reversible Markov chains can be viewed as random walks on undirected graphs, and symmetric Markov chains, as random walks on regular symmetric graphs. In this paper we'll formulate the results in terms of random walks, and mostly restrict our attention to the undirected case. 2 L. Lovv asz Random walks arise in many models in mathematics and physics. In fact, this is one of those notions that tend to pop up everywhere once you begin to look for them. For example, consider the shuuing of a deck of cards. Construct a graph whose nodes are all permutations of the deck, and two of them are adjacent if they come by one shuue move (depending on how you shuue). Then repeated shuue moves correspond to a random walk on this graph (see Diaconis 20]). The Brownian motion of a dust particle is random walk in the room. Models in statistical mechanics can be viewed as random walks on the set of states. The classical theory of random walks deals with random walks on simple , but innnite graphs, like grids, and studies their qualitative behaviour: does the random walk return to its starting point with probability one? does it return innnitely often? For example, PP olya (1921) proved that …
Co-management: concepts and methodological implications.

Co-management, or the joint management of the commons, is often formulated in terms of some arrangement of power sharing between the State and a community of resource users. In reality, there often are multiple local interests and multiple government agencies at play, and co-management can hardly be understood as the interaction of a unitary State and a homogeneous community. An approach focusing on the legal aspects of co-management, and emphasizing the formal structure of arrangements (how governance is configured) runs the risk of neglecting the functional side of co-management. An alternative approach is to start from the assumption that co-management is a continuous problem-solving process, rather than a fixed state, involving extensive deliberation, negotiation and joint learning within problem-solving networks. This presumption implies that co-management research should preferably focus on how different management tasks are organized and distributed concentrating on the function, rather than the structure, of the system. Such an approach has the effect of highlighting that power sharing is the result, and not the starting point, of the process. This kind of research approach might employ the steps of (1) defining the social-ecological system under focus; (2) mapping the essential management tasks and problems to be solved; (3) clarifying the participants in the problem-solving processes; (4) analyzing linkages in the system, in particular across levels of organization and across geographical space; (5) evaluating capacity-building needs for enhancing the skills and capabilities of people and institutions at various levels; and (6) prescribing ways to improve policy making and problem-solving.
The systemizing quotient: an investigation of adults with Asperger syndrome or high-functioning autism, and normal sex differences.

Systemizing is the drive to analyse systems or construct systems. A recent model of psychological sex differences suggests that this is a major dimension in which the sexes differ, with males being more drawn to systemize than females. Currently, there are no self-report measures to assess this important dimension. A second major dimension of sex differences is empathizing (the drive to identify mental states and respond to these with an appropriate emotion). Previous studies find females score higher on empathy measures. We report a new self-report questionnaire, the Systemizing Quotient (SQ), for use with adults of normal intelligence. It contains 40 systemizing items and 20 control items. On each systemizing item, a person can score 2, 1 or 0, so the SQ has a maximum score of 80 and a minimum of zero. In Study 1, we measured the SQ of n = 278 adults (114 males, 164 females) from a general population, to test for predicted sex differences (male superiority) in systemizing. All subjects were also given the Empathy Quotient (EQ) to test if previous reports of female superiority would be replicated. In Study 2 we employed the SQ and the EQ with n = 47 adults (33 males, 14 females) with Asperger syndrome (AS) or high-functioning autism (HFA), who are predicted to be either normal or superior at systemizing, but impaired at empathizing. Their scores were compared with n = 47 matched adults from the general population in Study 1. In Study 1, as predicted, normal adult males scored significantly higher than females on the SQ and significantly lower on the EQ. In Study 2, again as predicted, adults with AS/HFA scored significantly higher on the SQ than matched controls, and significantly lower on the EQ than matched controls. The SQ reveals both a sex difference in systemizing in the general population and an unusually strong drive to systemize in AS/HFA. These results are discussed in relation to two linked theories: the 'empathizing-systemizing' (E-S) theory of sex differences and the extreme male brain (EMB) theory of autism.
Diagnosis and debugging of programmable logic controller control programs by neural networks

Ladder logic diagram (LLD) as the interfacing programming language of programmable logic controllers (PLCs) is utilized in modern discrete event control systems. However, LLD is hard to debug and maintain in practice. This is due to many factors such as non-structured nature of LLD, the LLD programmers' background, and the huge sizes of real world LLD. In this paper, we introduce a recurrent neural network (RNN) based technique for PLC program diagnosis. A manufacturing control system example has been presented to illustrate the applicability of the proposed algorithm. This method could be very advantageous in reducing the complexity in PLC control programs diagnosis because of the ease of use of the RNN compared to debugging the LLD code.
Common method biases in behavioral research: a critical review of the literature and recommended remedies.

Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
Crime Scene Reconstruction: Online Gold Farming Network Analysis

Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.
Dual-wideband log-periodic antennas

Two log-periodic (LP) antenna configurations able to suppress a specific frequency band are introduced in this paper. LP antennas belong to the class of frequency independent antennas and are able to achieve multi-decade wide bandwidths. We start with a parametric study of a traditional planar curved tooth LP antenna aimed in determining the means for achieving specific bandwidth. Two techniques for accomplishing consistent near/far-field dual-band performance are introduced next. The first is a LP antenna integrated with a dual-wideband feed line, and the second one is a LP with removed resonant teeth. Antennas and feeds are designed on a microstrip substrate and method of moments modeling within FEKO is used to demonstrate their performance. Required dual-band operation is 1.8-5 GHz and 7-11 GHz.
ANE: Network Embedding via Adversarial Autoencoders

Network embedding is an important method to learn low-dimensional representations of vertexes in network, whose goal is to capture and preserve the highly non-linear network structures. Here, we propose an Adversarial autoencoders based Network Embedding method (ANE for short), which utilizes the rencently proposed adversarial autoencoders to perform variational inference by matching the aggregated posterior of low-dimensional representations of vertexes with an arbitraray prior distribution. This framework introduces adversarial regularization to autoencoders. And it is able to attaches the latent representations of similar vertexes to each other and thus prevents the manifold fracturing problem that is typically encountered in the embeddings learnt by the autoencoders. Experiments demonstrate the effictiveness of ANE on link prediction and multi-label classification on three real-world information networks.
Managing information systems security: a soft approach

The increasing rate of reported computer security problems suggests that the highly structured and technical traditional approaches to the management of IS security do not appear to be successful. This paper presents the Orion Strategy, a participative approach to the planning and management of information security in organisations. The details of this approach are discussed along with an overview of its pilot implementation within an Australian organisation. The findings from the study indicate that a high level of user participation in the planning and management of security results in raised awareness of security issues and an ownership of responsibility for the successful operations of chosen security measures.
Sensitivity and Generalization in Neural Networks: an Empirical Study

In practice it is often found that large over-parameterized neural networks generalize better than their smaller counterparts, an observation that appears to conflict with classical notions of function complexity, which typically favor smaller models. In this work, we investigate this tension between complexity and generalization through an extensive empirical exploration of two natural metrics of complexity related to sensitivity to input perturbations. Our experiments survey thousands of models with various fully-connected architectures, optimizers, and other hyper-parameters, as well as four different image classification datasets. We find that trained neural networks are more robust to input perturbations in the vicinity of the training data manifold, as measured by the norm of the input-output Jacobian of the network, and that it correlates well with generalization. We further establish that factors associated with poor generalization – such as full-batch training or using random labels – correspond to lower robustness, while factors associated with good generalization – such as data augmentation and ReLU non-linearities – give rise to more robust functions. Finally, we demonstrate how the input-output Jacobian norm can be predictive of generalization at the level of individual test points.
Electrowetting for Tunable Microoptics

The design, fabrication, and optical properties of tunable liquid microoptical components based on electrowetting are presented. Two optical microsystems are discussed: a single liquid microlens with tunable focal length, centered and stabilized in a micromachined positioning structure, and laterally repositionable tunable liquid microlens arrays. The latter employs electrowetting for lateral movement as well as focal length tuning of the optical elements. Through the use of novel microstructuring techniques, a high positioning accuracy may be achieved with low electrode count.
Matchmaking in multi-player on-line games: studying user traces to improve the user experience

Designing and implementing a quality matchmaking service for Multiplayer Online Games requires an extensive knowledge of the habits, behaviors and expectations of the players. Gathering and analyzing traces of real games offers insight on these matters, but game server providers are very protective of such data in order to deter possible reuse by the competition and to prevent cheating. We circumvented this issue by gathering public data from a League of Legends server (information over more than 28 million game sessions). In this paper, we present our database which is freely available online, and we detail the analysis and conclusions we draw from this data regarding the expected requirements for the matchmaking service.
Photo-Detectors for Time of Flight Positron Emission Tomography (ToF-PET)

We present the most recent advances in photo-detector design employed in time of flight positron emission tomography (ToF-PET). PET is a molecular imaging modality that collects pairs of coincident (temporally correlated) annihilation photons emitted from the patient body. The annihilation photon detector typically comprises a scintillation crystal coupled to a fast photo-detector. ToF information provides better localization of the annihilation event along the line formed by each detector pair, resulting in an overall improvement in signal to noise ratio (SNR) of the reconstructed image. Apart from the demand for high luminosity and fast decay time of the scintillation crystal, proper design and selection of the photo-detector and methods for arrival time pick-off are a prerequisite for achieving excellent time resolution required for ToF-PET. We review the two types of photo-detectors used in ToF-PET: photomultiplier tubes (PMTs) and silicon photo-multipliers (SiPMs) with a special focus on SiPMs.
Ending the war between Sales & Marketing.

Sales departments tend to believe that marketers are out of touch with what's really going on in the marketplace. Marketing people, in turn, believe the sales force is myopic--too focused on individual customer experiences, insufficiently aware of the larger market, and blind to the future. In short, each group undervalues the other's contributions. Both stumble (and organizational performance suffers) when they are out of sync. Yet few firms seem to make serious overtures toward analyzing and enhancing the relationship between these two critical functions. Curious about the misalignment between Sales and Marketing, the authors interviewed pairs of chief marketing officers and sales vice presidents to capture their perspectives. They looked in depth at the relationship between Sales and Marketing in a variety of companies in different industries. Their goal was to identify best practices that could enhance the joint performance and increase the contributions of these two functions. Among their findings: The marketing function takes different forms in different companies at different product life cycle stages. Marketing's increasing influence in each phase of an organization's growth profoundly affects its relationship with Sales. The strains between Sales and Marketing fall into two main categories: economic (a single budget is typically divided, between Sales and Marketing, and not always evenly) and cultural (the two functions attract very different types of people who achieve success by spending their time in very different ways). In this article, the authors describe the four types of relationships Sales and Marketing typically exhibit. They provide a diagnostic to help readers assess their companies' level of integration, and they offer recommendations for more closely aligning the two functions.