nils-herrmann/clean_scirepeval huggingface dataset:
SNOW 2014 Data Challenge: Assessing the Performance of News Topic Detection Methods in Social Media

The SNOW 2014 Data Challenge aimed at creating a public benchmark and evaluation resource for the problem of topic detection in streams of social content. In particular, given a set of tweets spanning a time interval of interest, the Challenge required the extraction of the most significant news topics in short timeslots within the selected interval. Here, we provide details with respect to the Challenge definition, the data collection and evaluation process, and the results achieved by the 11 teams that participated in it, along with a concise retrospective analysis of the main conclusions and arising issues.
Platform-as-a-Service (PaaS): The Next Hype of Cloud Computing

Cloud Computing is expected to become the driving force of information technology to revolutionize the future. Presently number of companies is trying to adopt this new technology either as service providers, enablers or vendors. In this way the cloud market is estimated be likely to emerge at a remarkable rate. Under the whole cloud umbrella, PaaS seems to have a relatively small market share. However, it is expected to offer much more as it is compared with its counterparts SaaS and IaaS. This paper is aimed to assess and analyze the future of PaaS technology. Year 2015 named as “the year of PaaS”. It means that PaaS technology has established strong roots and ready to hit the market with better technology services. This research will discuss future PaaS market trends, growth and business competitors. In the current dynamic era, several companies in the market are offering PaaS services. This research will also outline some of the top service providers (proprietary & open source) to discuss their current technology status and present a futuristic look into their services and business strategies. Analysis of the present and future PaaS technology infrastructure will also be a major discussion in this paper.
Big data analytics and firm performance : effects of dynamic capabilities

Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed. Disciplines Business Publication Details Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S. Ji-fan., Dubey, R. & Childe, S. J. (2017). Big data analytics and firm performance: effects of dynamic capabilities.  Angappa Gunasekaran, Shahriar Akter, Steven Ji-Fan Ren, Rameshwar Dubey, and Stephen J. Childe This journal article is available at Research Online: http://ro.uow.edu.au/buspapers/977
Modelo de Impacto del Consumo Masivo de Productos y Servicios Digitalizados

Resumen—La actual revolución que propone Internet ofrece una serie de productos y servicios digitalizados que circulan en un medio completamente abierto, sin reglas ni reglamentaciones, entre la masa de usuarios. Algunos de los cuales evidencian una serie de nuevos conflictos inherentes al comportamiento, la personalidad y otras afecciones físicas, manifestando un tipo de ciber-adicción. En este trabajo, el autor propone un modelo conceptual para evaluar el impacto económico, ambiental y social del consumo masivo de estas nuevas tecnologías, centrándose en los aspectos relacionados con negocios de comercio electrónico escalable.
The outcast-lash-out effect in youth: alienation increases aggression following peer rejection.

Although there are good theoretical reasons to believe that youth who are high in alienation (i.e., estranged from society, significant others, and themselves) are prone to behave aggressively, empirical evidence is lacking. The present experiment tested whether alienation moderates the effects of acute peer rejection on aggression in youth. Participants (N = 121; mean age = 11.5 years) completed a personal profile (e.g., "How do you describe yourself?") that was allegedly evaluated online by a panel of peer judges. After randomly receiving negative or positive feedback from peer judges, participants were given the opportunity to aggress against them (i.e., by reducing their monetary reward and by posting negative comments about them online). As predicted, alienation increased participants' aggression against peers who had rejected them, but not against peers who had praised them, even after controlling for peer-nominated chronic rejection and peer-nominated aggression. Thus, alienated youth are more aggressive than others when they experience acute peer rejection.
Product Geometric Crossover for the Sudoku Puzzle

Geometric crossover is a representation-independent definition of crossover based on the distance of the search space interpreted as a metric space. It generalizes the traditional crossover for binary strings and other important recombination operators for the most used representations. Using a distance tailored to the problem at hand, the abstract definition of crossover can be used to design new problem specific crossovers that embed problem knowledge in the search. In recent work, we have introduced the important notion of product geometric crossover that enables the construction of new geometric crossovers combining preexisting geometric crossovers in a simple way. In this paper, we use it to design an evolutionary algorithm to solve the Sudoku puzzle. The different types of constraints make Sudoku an interesting study case for crossover design. We conducted extensive experimental testing and found that, on medium and hard problems, the new geometric crossovers perform significantly better than hill-climbers and mutations alone.
Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

Cooling system plays a critical role in a modern data center (DC). Developing an optimal control policy for DC cooling system is a challenging task. The prevailing approaches often rely on approximating system models that are built upon the knowledge of mechanical cooling, electrical and thermal management, which is difficult to design and may lead to suboptimal or unstable performances. In this paper, we propose utilizing the large amount of monitoring data in DC to optimize the control policy. To do so, we cast the cooling control policy design into an energy cost minimization problem with temperature constraints, and tap it into the emerging deep reinforcement learning (DRL) framework. Specifically, we propose an end-toend cooling control algorithm (CCA) that is based on the actorcritic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm. In the proposed CCA, an evaluation network is trained to predict an energy cost counter penalized by the cooling status of the DC room, and a policy network is trained to predict optimized control settings when gave the current load and weather information. The proposed algorithm is evaluated on the EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. Our results show that the proposed CCA can achieve about 11% cooling cost saving on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study, we propose a de-underestimation validation mechanism as we cannot directly test the algorithm on a real DC. Even though with DUE the results are conservative, we can still achieve about 15% cooling energy saving on the NSCC data trace if we set the inlet temperature threshold at 26.6 degree Celsius.
Two time-efficient gibbs sampling inference algorithms for biterm topic model

Biterm Topic Model (BTM) is an effective topic model proposed to handle short texts. However, its standard gibbs sampling inference method (StdBTM) costs much more time than that (StdLDA) of Latent Dirichlet Allocation (LDA). To solve this problem we propose two time-efficient gibbs sampling inference methods, SparseBTM and ESparseBTM, for BTM by making a tradeoff between space and time consumption in this paper. The idea of SparseBTM is to reduce the computation in StdBTM by both recycling intermediate results and utilizing the sparsity of count matrix NWT $\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}$. Theoretically, SparseBTM reduces the time complexity of StdBTM from O(|B| K) to O(|B| K w ) which scales linearly with the sparsity of count matrix NWT $\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}$ (K w ) instead of the number of topics (K) (K w < K, K w is the average number of non-zero topics per word type in count matrix NWT $\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}$). Experimental results have shown that in good conditions SparseBTM is approximately 18 times faster than StdBTM. Compared with SparseBTM, ESparseBTM is a more time-efficient gibbs sampling inference method proposed based on SparseBTM. The idea of ESparseBTM is to reduce more computation by recycling more intermediate results through rearranging biterm sequence. In theory, ESparseBTM reduces the time complexity of SparseBTM from O(|B|K w ) to O(R|B|K w ) (0 < R < 1, R is the ratio of the number of biterm types to the number of biterms). Experimental results have shown that the percentage of the time efficiency improved by ESparseBTM on SparseBTM is between 6.4% and 39.5% according to different datasets.
Real-time 3D Walking Pattern Generation for a Biped Robot with Telescopic Legs

Meltran V, a new biped robot with telescopic legs, is introduced. For 3D walking control of the robot we analyze the dynamics of a three-dimensional inverted pendulum in which motion is constrained to move along an arbitrarily de ned plane. From this analysis we obtain simple linear dynamics, the ThreeDimensional Linear Inverted Pendulum Mode (3DLIPM). Using a real-time control method based on 3DLIPM, the Meltran V robot successfully demonstrated 3D dynamic walking without the use of any prepared trajectories.
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images

There is a high demand of 3D data for 360◦ panoramic images and videos, pushed by the growing availability on the market of specialized hardware for both capturing (e.g., omni-directional cameras) as well as visualizing in 3D (e.g., head mounted displays) panoramic images and videos. At the same time, 3D sensors able to capture 3D panoramic data are expensive and/or hardly available. To fill this gap, we propose a learning approach for panoramic depth map estimation from a single image. Thanks to a specifically developed distortion-aware deformable convolution filter, our method can be trained by means of conventional perspective images, then used to regress depth for panoramic images, thus bypassing the effort needed to create annotated panoramic training dataset. We also demonstrate our approach for emerging tasks such as panoramic monocular SLAM, panoramic semantic segmentation and panoramic style transfer.
Beam-steerable patch antenna array using parasitic coupling and reactive loading

A parasitically-coupled and reactively-loaded patch antenna array is demonstrated to achieve beam steering in an analog manner. The strong parasitic coupling between closely-spaced patch antennas is used to form a phased array with a single driven element. The phase shift between the array elements is adjustable by changing the reactive loading on the parasitic patch antenna. A prototype of this antenna array is demonstrated at 2.3 GHz.
PIN Skimming: Exploiting the Ambient-Light Sensor in Mobile Devices

The pervasive usage of mobile devices, i.e., smartphones and tablet computers, and their vast amount of sensors represent a plethora of side channels posing a serious threat to the user's privacy and security. In this paper, we propose a new type of side channel which is based on the ambient-light sensor employed in today's mobile devices. While recent advances in this area of research focused on the employed motion sensors and the camera as well as the sound, we investigate a less obvious source of information leakage, namely the ambient light. We successfully demonstrate that minor tilts and turns of mobile devices cause variations of the ambient-light sensor information. Furthermore, we show that these variations leak enough information to infer a user's personal identification number (PIN) input based on a set of known PINs. Our results clearly show that we are able to determine the correct PIN---out of a set of 50 random PINs---within the first ten guesses about 80% of the time. In contrast, the chance of finding the right PIN by randomly guessing ten PINs would be 20%. Since the data required to perform such an attack can be gathered without any specific permissions or privileges, the presented attack seriously jeopardizes the security and privacy of mobile-device owners.
AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks

ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed empirically. Mathematically, shuffling is a multiplication by a permutation matrix. In this paper, we propose to automate channel shuffling by learning permutation matrices in network training. We introduce an exact Lipschitz continuous nonconvex penalty so that it can be incorporated in the stochastic gradient descent to approximate permutation at high precision. Exact permutations are obtained by simple rounding at the end of training and are used in inference. The resulting network, referred to as AutoShuffleNet, achieved improved classification accuracies on CIFAR-10 and ImageNet data sets. In addition, we found experimentally that the standard convex relaxation of permutation matrices into stochastic matrices leads to poor performance. We prove theoretically the exactness (error bounds) in recovering permutation matrices when our penalty function is zero (very small). We present examples of permutation optimization through graph matching and two-layer neural network models where the loss functions are calculated in closed analytical form. In the examples, convex relaxation failed to capture permutations whereas our penalty succeeded.
Massively Parallel Parsing: A Strongly Interactive Model of Natural Language Interpretation

This is ct description of research in developing ct natural language processing system with modular knowledge sources but strongly interactive processing. The system offers insights into o variety of linguistic phenomeno and allows easy testing of a variety of hypotheses. Language interpretotion takes place on ct activation network which is dynamically created from input, recent context, ond long-term knowledge. Initially ambiguous and unstable, the network settles on 0 single interpretation, using 0 parallel, analog relaxation process. We 0150 describe a parallel model for the representation of context ond of the priming of concepts. Exomples illustrating contextual influence on meaning interpretation and “semantic garden poth” sentence processing, among other issues, ore included.
A Network Slicing Framework for End-to-End QoS Provisioning in 5 G Networks

With software-defined networking (SDN) and network function virtualization (NFV) technologies, network slicing emerges as a promising solution for resource orchestration to achieve quality-of-service (QoS) isolation among customized services in 5G networks. In this article, we propose a comprehensive network slicing framework for end-to-end (E2E) QoS provisioning, with the consideration of differentiated resource types in both wireless and wired network domains. For the wireless network domain, a dynamic radio resource slicing scheme is proposed, in which the overall bandwidth resources are sliced for different BSs to maximize the network utility. The optimal bandwidth slicing ratios are dynamically adjusted based on instantaneous network load conditions. For the wired network domain, bottleneck-resource generalized processor sharing (BR-GPS) is employed as a bi-resource slicing scheme among multiple traffic flows traversing an NFV node. In addition to the property of bottleneck-resource fair allocation with high resource utilization, we show that the BR-GPS minimizes packet queueing delay for each flow at the outgoing link of the NFV node. Some open research problems regarding network slicing are discussed. A case study is presented to demonstrate the effectiveness of the proposed network slicing framework.
Short-Term Travel Time Prediction Using A Time-Varying Coefficient Linear Model ?

Effective prediction of travel times is central to many advanced traveler information and transportation management systems. In this article we propose a method to predict freeway travel times using a linear model in which the coefficients vary as smooth functions of the departure time. The method is straightforward to implement, computationally efficient and applicable to widely available freeway sensor data. We demonstrate its effectiveness by applying the method to two real-life loop detector data sets.
The role of the inferior frontal junction area in cognitive control

Cognitive control processes refer to our ability to coordinate thoughts and actions in accordance with internal goals. In the fronto-lateral cortex such processes have been primarily related to mid-dorsolateral prefrontal cortex (mid-DLPFC). However, recent brain-imaging and meta-analytic studies suggest that a region located more posterior in the fronto-lateral cortex plays a pivotal role in cognitive control as well. This region has been termed the inferior frontal junction area and can be functionally and structurally distinguished from mid-DLPFC.
Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization

There is a natural correlation between the visual and auditive elements of a video. In this work, we use this correlation in order to learn strong and general features via cross-modal self-supervision with carefully chosen neural network architectures and calibrated curriculum learning. We suggest that this type of training is an effective way of pretraining models for further pursuits in video understanding, as they achieve on average 14.8% improvement over models trained from scratch. Furthermore, we demonstrate that these general features can be used for audio classification and perform on par with state-of-the-art results. Lastly, our work shows that using cross-modal self-supervision for pretraining is a good starting point for the development of multi-sensory models.
Optimization Using Simulation of Traffic Light Signal Timings

Traffic congestion has become a great challenge and a large burden on both the governments and the citizens in vastly populated cities. The main problem, originally initiated by several factors, continues to threaten the stability of modern cities and the livelihood of its habitants. Hence, improving the control strategies that govern the traffic operations is a powerful solution that can solve the congestion problem. These improvements can be achieved by enhancing the traffic control performance through adjusting the traffic signal timings. This paper focuses on finding various solutions for the addressed problem through the optimization using simulation of traffic signal timings under oversaturated conditions. The system under study is an actual road network in Alexandria, Egypt; where, numerous data have been collected in different time intervals. A series of computer simulation models to represent the actual system as well as proposed solutions have been developed using the ExtendSim simulation environment. Furthermore, an evolutionary optimizer is utilized to attain a set of optimum/near-optimum signal timings to minimize the total time in system of the vehicles, resulting in an improved performance of the road network. Analysis of experimentation results shows that the adopted methodology optimizes the vehicular flow in a multiple-junction urban traffic network.
A Compact Circularly Polarized Cross-Shaped Slotted Microstrip Antenna

A compact cross-shaped slotted microstrip patch antenna is proposed for circularly polarized (CP) radiation. A symmetric, cross shaped slot is embedded along one of the diagonal axes of the square patch for CP radiation and antenna size reduction. The structure is asymmetric (unbalanced) along the diagonal axes. The overall size of the antenna with CP radiation can be reduced by increasing the perimeter of the symmetric cross-shaped slot within the first patch quadrant of the square patch. The performance of the CP radiation is also studied by varying the size and angle variation of the cross-shaped slot. A measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz is achieved with the CP cross-shaped slotted microstrip antenna, with an 18.0 MHz 10-dB return-loss bandwidth. The measured boresight gain is more than 3.8 dBic over the operating band, while the overall antenna volume is 0.273λo × 0.273λo × 0.013λo (λο operating wavelength at 910 MHz).
A further step forward in measuring journals' scientific prestige: The SJR2 indicator

A new size-independent indicator of scientific journal prestige, the SJR2 indicator, is proposed. This indicator takes into account not only the prestige of the citing scientific journal but also its closeness to the cited journal using the cosine of the angle between the vectors of the two journals' cocitation profiles. To eliminate the size effect, the accumulated prestige is divided by the fraction of the journal's citable documents, thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores. Its method of computation is described, and the results of its implementation on the Scopus 2008 dataset is compared with those of an ad hoc Journal Impact Factor, JIF(3y), and SNIP, the comparison being made both overall and within specific scientific areas. All three, the SJR2 indicator, the SNIP indicator and the JIF distributions, were found to fit well to a logarithmic law. Although the three metrics were strongly correlated, there were major changes in rank. In addition, the SJR2 was distributed more equalized than the JIF by Subject Area and almost as equalized as the SNIP, and better than both at the lower level of Specific Subject Areas. The incorporation of the cosine increased the values of the flows of prestige between thematically close journals.
I2oTology - Tracking-Oriented Ontology

To join the Internet of Things (IoT) and Ontology concepts today it is becoming a good strategy to save sensors and Smart Objects (SO) information using all the semantic capabilities and ontology inferences to improve and give some intelligence at the information manipulation, IoT-Lite and SSN (Semantic Sensor Network) are examples of ontologies for IoT. This paper presents the I2oTology, which is a tracking-oriented ontology. The I2oTology purpose is to presents a semantic aimed at tracking smart objects based on some IoT-Lite classes. It was made a simple test with this ontology but there is some classes, properties and situations to be tested and also to know how much the ontology is right, these topics will be considered at the future work.