nils-herrmann/clean_scirepeval huggingface dataset:
Toward an understanding of flow in video games

In the domain of computer games, research into the interaction between player and game has centred on 'enjoyment', often drawing in particular on optimal experience research and Csikszentmihalyi's 'Flow theory'. Flow is a well-established construct for examining experience in any setting and its application to game-play is intuitive. Nevertheless, it's not immediately obvious how to translate between the flow construct and an operative description of game-play. Previous research has attempted this translation through analogy. In this article we propose a practical, integrated approach for analysis of the mechanics and aesthetics of game-play, which helps develop deeper insights into the capacity for flow within games.
 The relationship between player and game, characterized by learning and enjoyment, is central to our analysis. We begin by framing that relationship within Cowley's user-system-experience (USE) model, and expand this into an information systems framework, which enables a practical mapping of flow onto game-play. We believe this approach enhances our understanding of a player's interaction with a game and provides useful insights for games' researchers seeking to devise mechanisms to adapt game-play to individual players.
Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions.

The purpose is to develop and evaluate the ability of the computer-aided diagnosis (CAD) methods that apply texture analysis and pattern classification to differentiate malignant and benign bone and soft-tissue lesions on 18F-fluorodeoxy-glucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) images.



Subjects were 103 patients with 59 malignant and 44 benign bone and soft tissue lesions larger than 25 mm in diameter. Variable texture parameters of standardized uptake values (SUV) and CT Hounsfield unit values were three-dimensionally calculated in lesional volumes-of-interest segmented on PET/CT images. After selection of a subset of the most optimal texture parameters, a support vector machine classifier was used to automatically differentiate malignant and benign lesions. We developed three kinds of CAD method. Two of them utilized only texture parameters calculated on either CT or PET images, and the other one adopted the combined PET and CT texture parameters. Their abilities of differential diagnosis were compared with the SUV method with an optimal cut-off value of the maximum SUV.



The CAD methods utilizing only optimal PET (or CT) texture parameters showed sensitivity of 83.05 % (81.35 %), specificity of 63.63 % (61.36 %), and accuracy of 74.76 % (72.82 %). Although the ability of differential diagnosis by PET or CT texture analysis alone was not significantly different from the SUV method whose sensitivity, specificity, and accuracy were 64.41, 61.36, and 63.11 % (the optimal cut-off SUVmax was 5.4 ± 0.9 in the 10-fold cross-validation test), the CAD method with the combined PET and CT optimal texture parameters (PET: entropy and coarseness, CT: entropy and correlation) exhibited significantly better performance compared with the SUV method (p = 0.0008), showing a sensitivity of 86.44 %, specificity of 77.27 %, and accuracy of 82.52 %.



The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.
Overview of meta-analyses on early intensive behavioral intervention for young children with autism spectrum disorders.

This paper presents an overview of 5 meta-analyses of early intensive behavioral intervention (EIBI) for young children with autism spectrum disorders (ASDs) published in 2009 and 2010. There were many differences between meta-analyses, leading to different estimates of effect and overall conclusions. The weighted mean effect sizes across meta-analyses for IQ and adaptive behavior ranged from g = .38-1.19 and g = .30-1.09, respectively. Four of five meta-analyses concluded EIBI was an effective intervention strategy for many children with ASDs. A discussion highlighting potential confounds and limitations of the meta-analyses leading to these discrepancies and conclusions about the efficacy of EIBI as an intervention for young children with ASDs are provided.
Verified quantum information scrambling

Quantum scrambling is the dispersal of local information into many-body quantum entanglements and correlations distributed throughout an entire system. This concept accompanies the dynamics of thermalization in closed quantum systems, and has recently emerged as a powerful tool for characterizing chaos in black holes1–4. However, the direct experimental measurement of quantum scrambling is difficult, owing to the exponential complexity of ergodic many-body entangled states. One way to characterize quantum scrambling is to measure an out-of-time-ordered correlation function (OTOC); however, because scrambling leads to their decay, OTOCs do not generally discriminate between quantum scrambling and ordinary decoherence. Here we implement a quantum circuit that provides a positive test for the scrambling features of a given unitary process5,6. This approach conditionally teleports a quantum state through the circuit, providing an unambiguous test for whether scrambling has occurred, while simultaneously measuring an OTOC. We engineer quantum scrambling processes through a tunable three-qubit unitary operation as part of a seven-qubit circuit on an ion trap quantum computer. Measured teleportation fidelities are typically about 80 per cent, and enable us to experimentally bound the scrambling-induced decay of the corresponding OTOC measurement. A quantum circuit in an ion-trap quantum computer provides a positive test for the scrambling features of a given unitary process.
Sepic Topology Based High Step-Up Step down Soft Switching Bidirectional DC-DC Converter for Energy Storage Applications

SEPIC topology based bidirectional DC-DC Converter is proposed for interfacing energy storage elements such as batteries & super capacitors with various power systems. This proposed bidirectional DC-DC converter acts as a buck boost where it changes its output voltage according to its duty cycle. An important factor is used to increase the voltage conversion ratio as well as it achieves high efficiency. In the proposed SEPIC based BDC converter is used to increase the voltage proposal of this is low voltage at the input side is converted into a very high level at the output side to drive the HVDC smart grid. In this project PIC microcontro9 ller is used to give faster response than the existing system. The proposed scheme ensures that the voltage on the both sides of the converter is always matched thereby the conduction losses can be reduced to improve efficiency. MATLAB/Simulink software is utilized for simulation. The obtained experimental results show the functionality and feasibility of the proposed converter.
The role of mitochondria in aging.

Over the last decade, accumulating evidence has suggested a causative link between mitochondrial dysfunction and major phenotypes associated with aging. Somatic mitochondrial DNA (mtDNA) mutations and respiratory chain dysfunction accompany normal aging, but the first direct experimental evidence that increased mtDNA mutation levels contribute to progeroid phenotypes came from the mtDNA mutator mouse. Recent evidence suggests that increases in aging-associated mtDNA mutations are not caused by damage accumulation, but rather are due to clonal expansion of mtDNA replication errors that occur during development. Here we discuss the caveats of the traditional mitochondrial free radical theory of aging and highlight other possible mechanisms, including insulin/IGF-1 signaling (IIS) and the target of rapamycin pathways, that underlie the central role of mitochondria in the aging process.
A microstrip periodic leaky-wave antenna optimized for broadside scanning

In this paper a microstrip periodic leaky-wave antenna optimized for radiation scanning through broadside is presented. This antenna is based on the excitation of a leaky mode that propagates along the structure, with radiation occurring from the -1 space harmonic. The specific geometry of the metalization inside the unit cell permits a continuous scanning from the backward to the forward quadrant without any degradation of the beam, avoiding the presence of open-stopband effects. This new design provides a valid alternative to metamaterial leaky-wave antennas already presented in the literature and demonstrates the possibility of obtaining a conventional microstrip periodic LWA operating in the -1 harmonic with a backfire-to-endfire frequency-scanning capability.
A Neural Sorting Network with O(1) Time Complexity

Analog computation architectures such as artificial neural network models have received phenomenal attention lately. Massive parallel processing is natural in neural networks, which also meets the development trend of computer science. Because parallelism is a straight way to speed up computation. Thus, considering parallel algorithms on neural networks is quite reasonable. Neural network models have been shown to be able to find “good” solutions for some optimization problems in a short time [51. But, it cannot guarantee finding the real optimal solution except by techniques similar to simulated annealing. Under such circumstances, there is no systematic way to determine the annealing schedule and the time needed for convergence. Vergis et al. [ll] showed that analog computation can be simulated efficiently by a Turing machine in polynomial time. Unless P = NP, NP-complete problems cannot be solved by analog computation in polynomial time. But, from the view of time complexity, analog computation is better than digital computation in one perspective. The basic operation
Implementing openflow based distributed firewall

SDN is an emerging technology which is going to drive next generation networks. Lot of companies and organizations has started using SDN applications. It is giving network administrators the flexibility in implementing their networks. But at the same time, it is bringing new security issues. To secure SDN networks, we need strong firewall application. Already some firewall applications are there but they suffer from certain shortcomings. One of the main drawbacks of existing firewall solutions is that they suffer from single point of failure due to their centralized nature and overloading of rules in single device. Other drawback of existing firewall is that they are mostly layer 2 firewalls. In this paper, we are implementing Distributed Firewall where every OpenFlow switch in a network can acts as a firewall. Plus this firewall will be capable of handling TCP, UDP and ICMP Traffic. We have tested this firewall using Mininet Emulator installed in Ubuntu 14.04 Linux installed under VirtualBox virtualization solution. We are using python based POX controller. This work is extension of our earlier work on programmable firewalls.
Self-Paced Learning with Adaptive Deep Visual Embeddings

Selecting the most appropriate data examples to present a deep neural network (DNN) at different stages of training is an unsolved challenge. Though practitioners typically ignore this problem, a non-trivial data scheduling method may result in a significant improvement in both convergence and generalization performance. In this paper, we introduce Self-Paced Learning with Adaptive Deep Visual Embeddings (SPL-ADVisE), a novel end-to-end training protocol that unites self-paced learning (SPL) and deep metric learning (DML). We leverage the Magnet Loss to train an embedding convolutional neural network (CNN) to learn a salient representation space. The student CNN classifier dynamically selects similar instance-level training examples to form a mini-batch, where the easiness from the cross-entropy loss and the true diverseness of examples from the learned metric space serve as sample importance priors. To demonstrate the effectiveness of SPL-ADVisE, we use deep CNN architectures for the task of supervised image classification on several coarseand fine-grained visual recognition datasets. Results show that, across all datasets, the proposed method converges faster and reaches a higher final accuracy than other SPL variants, particularly on fine-grained classes.
Conical monopole type UWB antenna with SRR to tune out the suppressed dual bands

In this paper, conical monopole type UWB antenna which suppress dual bands is proposed. The SSRs were arranged in such a way that the interaction of the magnetic field with them enables the UWB antenna to reject the dual bands using the resonance of SRRs. The proposed conical monopole antenna has a return loss less than -10dB and antenna gain greater than 5dB at 2GHz~11GHz frequency band, except the suppressed bands. The return loss and gain at WiMAX and WLAN bands is greater than -3dB and less than 0dB respectively.
Machine Learning Techniques for Intrusion Detection

An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. Most techniques used in today’s IDS are not able to deal with the dynamic and complex nature of cyber attacks on computer networks. Hence, efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication costs. In this paper, we study several such schemes and compare their performance. We divide the schemes into methods based on classical artificial intelligence (AI) and methods based on computational intelligence (CI). We explain how various characteristics of CI techniques can be used to build efficient IDS.
An Experimental Study on Wi-Fi Ad-Hoc Mode for Mobile Device-to-Device Video Delivery

The demand for video content is continuously increasing as video sharing on the Internet is becoming enormously popular recently. This demand, with its high bandwidth requirements, has a considerable impact on the load of the network infrastructure. As more users access videos from their mobile devices, the load on the current wireless infrastructure (which has limited capacity) will be even more significant. Based on observations from many local video sharing scenarios, in this paper, we study the tradeoffs of using Wi-Fi ad-hoc mode versus infrastructure mode for video streaming between adjacent devices. We thus show the potential of direct device-to-device communication as a way to reduce the load on the wireless infrastructure and to improve user experiences. Setting up experiments for WiFi devices connected in ad-hoc mode, we collect measurements for various video streaming scenarios and compare them to the case where the devices are connected through access points. The results show the improvements in latency, jitter and loss rate. More importantly, the results show that the performance in direct device-to-device streaming is much more stable in contrast to the access point case, where different factors affect the performance causing widely unpredictable qualities.
A fast-transient quasi-V2 switching buck regulator using AOT control

A switching regulator with quasi-V2 adaptive on-time (AOT) control, that provides a fast load transient response is proposed. A feed-forward path network allows the proposed switching regulator to achieve a fast transient response and stable operation without requiring an output capacitor with a large equivalent series resistance. The proposed AOT controller makes the switching frequency pseudo fixed in continuous conduction mode and works in pulse-frequency modulation mode under ultra-light load conditions. The AOT controller adjusts the on-time according to the supply voltage load current conditions. The measurement results verify that the switching regulator can operate under load currents of 5–800 mA for supply voltages of 3.3–4.2 V and an output voltage of 1.2 V. The recovery time is 3.6μs and the voltage drop is 55 mV when the load current is increased from 5 to 750 mA.
A Giant Hemangioma of the Tongue

Vascular abnormalities are relatively uncommon lesions, but head and neck is a common region for vascular malformation which is classified as benign tumors. In this paper, the authors report a rare presentation of vascular malformation in the tongue and its managements.  An 18 months 2 old child presented with a giant mass of tongue which caused functional and aesthetic problem. The rapid growth pattern of cavernous hemangioma was refractory to corticosteroid. The lesion was excised without any complication. Since the mass was so huge that not only filled entire oral cavity but was protruding outside, airway management was a great challenge for anesthesia plan and at the same time surgical technique was difficult to select.  Despite different recommended modalities in managing hemangiomas of the tongue, in cases of huge malformations, surgery could be the mainstay treatment and provided that critical care measures are taken in to account, could be performed very safely.
Rapid screening of toxic salbutamol, ractopamine, and clenbuterol in pork sample by high-performance liquid chromatography-UV method.

A rapid and simple high-performance liquid chromatography-UV method was developed for the separation and quantification of salbutamol, ractopamine, and clenbuterol in pork. A mixture of acetonitrile-formic acid-ammonium acetate was used as the mobile phase to separate three β-agonists on a C18 column with gradient. The effects of the addition of formic acid and ammonium acetate to mobile phases on the separation of β-agonists were investigated. These additives can greatly improve the resolution and sensitivity. Under the optimized chromatographic condition, this separation does not need extra sample preparation. Complete baseline separation of three β-agonists was achieved in < 20 minutes; the linear range is 0.2-50 μg/L with a correlation coefficient R2 value of > 0.99. Excellent method reproducibility was found by intra- and interday precisions with a relative standard deviation of < 3%. The detection limit (S/N = 3) was found to be <0.05 μg/L; this method can be used for routine screening of the β-agonist residues in foods of animal origin before being identified by confirmatory methods.
Bi-box Regression for Pedestrian Detection and Occlusion Estimation

Occlusions present a great challenge for pedestrian detection in practical applications. In this paper, we propose a novel approach to simultaneous pedestrian detection and occlusion estimation by regressing two bounding boxes to localize the full body as well as the visible part of a pedestrian respectively. For this purpose, we learn a deep convolutional neural network (CNN) consisting of two branches, one for full body estimation and the other for visible part estimation. The two branches are treated differently during training such that they are learned to produce complementary outputs which can be further fused to improve detection performance. The full body estimation branch is trained to regress full body regions for positive pedestrian proposals, while the visible part estimation branch is trained to regress visible part regions for both positive and negative pedestrian proposals. The visible part region of a negative pedestrian proposal is forced to shrink to its center. In addition, we introduce a new criterion for selecting positive training examples, which contributes largely to heavily occluded pedestrian detection. We validate the effectiveness of the proposed bi-box regression approach on the Caltech and CityPersons datasets. Experimental results show that our approach achieves promising performance for detecting both non-occluded and occluded pedestrians, especially heavily occluded ones.
PowerAI DDL

As deep neural networks become more complex and input data-sets grow larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, distributed Deep Learning at a massive scale is a critical capability, since it offers the potential to reduce the training time from weeks to hours. In this paper, we present a software-hardware co-optimized distributed Deep Learning system that can achieve near-linear scaling up to hundreds of GPUs. The core algorithm is a multi-ring communication pattern that provides a good tradeoff between latency and bandwidth and adapts to a variety of system configurations. The communication algorithm is implemented as a library for easy use. This library has been integrated into Tensorflow, Caffe, and Torch. We train Resnet-101 on Imagenet 22K with 64 IBM Power8 S822LC servers (256 GPUs) in about 7 hours to an accuracy of 33.8% validation accuracy. Microsoft’s ADAM  and Google’s DistBelief [11] results did not reach 30% validation accuracy for Imagenet 22K. Compared to Facebook’s recent paper  on 256 GPU training, we use a different communication algorithm, and our combined software and hardware system offers better communication overhead for Resnet50. A PowerAI DDL enabled version of Torch completed 90 epochs of training on Resnet 50 for 1K classes in 50 minutes using 64 IBM Power8 S822LC servers (256 GPUs).
The role of project management in ineffective decision making within Agile software development projects

30 April 2008  30 September 2008 2nd Revision: 18 June 2009  21 June 2009  Decision making in traditional software development lies with the project manager. In contrast, Agile software development teams are empowered to make decisions, while the role of project manager has changed from one of command and control (i.e. to make decisions and ensure they are implemented) to one of a facilitator. This article argues that decision making in software development is not characterised by a sequence of isolated or exclusive decisions; rather, decisions are inter-related, with each decision leading to further decisions, the chain of which often spans the entire duration of a project. Over this extended period, there are several potential factors that can negatively affect the efficacy of decision making by Agile teams. One of the findings of this exploratory longitudinal study is that the high level of empowerment of a cohesive software development team undertaking an Agile project may be one of these negative factors, as empowered, cohesive teams can exhibit problems such as groupthink or the Abilene Paradox. This article therefore argues that the role of project manager in Agile development initiatives needs to be reassessed, with project managers taking on the role of devil’s advocate in the decision-making process. European Journal of Information Systems (2009) 18, 372–383. doi:10.1057/ejis.2009.22; published online 28 July 2009