nils-herrmann/clean_scirepeval huggingface dataset:
Software-Defined Networking: A Comprehensive Survey

The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network’s control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms with a focus on aspects such as resiliency, scalability, performance, security, and dependabilityVas well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined
Learning from unstructured multimedia data

Information in today's world is highly heterogeneous and unstructured. Learning and inferring from such data is challenging and is an active research topic. In this paper, we present and investigate an approach to learning from heterogeneous and unstructured multimedia data. Inspired by approaches in many fields including computer vision, we investigate a histogram based approach to represent multimodal unstructured data. While existing works have predominantly focused on histogram based approaches for unimodal data, we present a methodology to represent unstructured multimodal data. We explain how to discover the prototypical features or codewords over which these histograms are built. We present experimental results on classification and retrieval tasks performed on the histogram based representation.
A PROGRAMMING INTERFACE FOR MEDICAL DIAGNOSIS PREDICTION

The aim of this paper is to present a programming interface that can be used for assisting physicians in medical diagnosis. The interface provides an original diagnosis technique based on relational association rules and a supervised learning method. Using the designed interface, we made an experiment for cancer diagnosis; the precision of the diagnosis on our testing data was 90%. The main advantage of the proposed interface is that can be use in diagnosis for every disease, and, much more, can be simply extended, by adding new symptom types and new relations between symptoms for the given disease.
Why are some people happier than others? The role of cognitive and motivational processes in well-being.

Addressing the question of why some people are happier than others is important for both theoretical and practical reasons and should be a central goal of a comprehensive positive psychology. Following a construal theory of happiness, the author proposes that multiple cognitive and motivational processes moderate the impact of the objective environment on well-being. Thus, to understand why some people are happier than others, one must understand the cognitive and motivational processes that serve to maintain, and even enhance, enduring happiness and transient mood. The author's approach has been to explore hedonically relevant psychological processes, such as social comparison, dissonance reduction, self-reflection, self-evaluation, and person perception, in chronically happy and unhappy individuals. In support of a construal framework, self-rated happy and unhappy people have been shown to differ systematically in the particular cognitive and motivational strategies they use. Promising research directions for positive psychology in pursuit of the sources of happiness, as well as the implications of the construal approach for prescriptions for enhancing well-being, are discussed.
WebOWL: A Semantic Web search engine development experiment

This paper presents WebOWL, an experiment in using the latest technologies to develop a Semantic Web search engine. WebOWL consists of a community of intelligent agents, acting as crawlers, that are able to discover and learn the locations of Semantic Web neighborhoods on the Web, a semantic database to store data from different ontologies, a query mechanism that supports semantic queries in OWL, and a ranking algorithm that determines the order of the returned results based on the semantic relationships of classes and individuals. The system has been implemented using Jade, Jena and the db4o object database engine and has successfully stored over one million OWL classes, individuals and properties.
The Effects of Personal Susceptibility and Social Support on Internet Addiction: an Application of Adler’s Theory of Individual Psychology

The aim of the present study was to examine the addictive effects of personal susceptibility and social support from family, friends, and significant other on Internet addiction. In this study, 207 medical students completed the UCLA Loneliness Scale, the Academic Expectations Stress Inventory, the Multidimensional Scale of Perceived Social Support, and the Young’s Diagnostic Questionnaire for Internet addiction. Participants were recruited via simple random sampling technique. Personal susceptibility variables such as loneliness, academic stress due to other expectations, and academic stress due to self-expectations were significantly positively correlated to, and all domains of social support were significantly negatively correlated to, Internet addiction. After adjusting for demographic variables, social support emerged as a significant predictor of Internet addiction in the hierarchical regression analysis. In support of Adler’s individual psychology theory, the present findings suggest that social support from family is a valuable adjunct to prevention and intervention programs aimed at alleviating Internet addiction in medical students.
Ventilation performance prediction for buildings : A method overview and recent applications

This paper presented an overview of the tools used to predict ventilation performance in buildings. The tools reviewed were analytical models, empirical models, small-scale experimental models, full-scale experimental models, multizone network models, zonal models, and Computational Fluid Dynamics (CFD) models. This review found that the analytical and empirical models had made minimal contributions to the research literature in the past year. The smalland full-scale experimental models were mainly used to generate data to validate numerical models. The multizone models were improving, and they were the main tool for predicting ventilation performance in an entire building. The zonal models had limited applications and could be replaced by the coarse-grid CFD models. The CFD models were most popular and contributed to 70 percent of the literature found in this review. Considerable efforts were still made to seek more reliable and accurate models. It has been a trend to improve their performance by coupling CFD with other building simulation models. The applications of CFD models were mainly for studying indoor air quality, natural ventilation, and stratified ventilation as they were difficult to be predicted by other models.
Simulation and experiment on SIW slot array antennas

By etching longitudinal slots on the top metallic surface of the substrate integrated waveguide (SIW), an integrated slot-array antenna is proposed in this letter. The whole antenna and feeding system are fabricated on a single substrate, which takes the advantage of small size, low profile, and low cost, etc. The design process and experimental results of a four-by-four SIW slot array antenna at X-band are presented.
An Ultra Low Energy 12-bit Rate-Resolution Scalable SAR ADC for Wireless Sensor Nodes

A resolution-rate scalable ADC for micro-sensor networks is described. Based on the successive approximation register (SAR) architecture, this ADC has two resolution modes: 12 bit and 8 bit, and its sampling rate is scalable, at a constant figure-of-merit, from 0-100 kS/s and 0-200 kS/s, respectively. At the highest performance point (i.e., 12 bit, 100 kS/s), the entire ADC (including digital, analog, and reference power) consumes 25 muW from a 1-V supply. The ADC's CMRR is enhanced by common-mode independent sampling and passive auto-zero reference generation. The efficiency of the comparator is improved by an analog offset calibrating latch, and the preamplifier settling time is relaxed by self-timing the bit-decisions. Prototyped in a 0.18-mum, 5M2P CMOS process, the ADC, at 12 bit, 100 kS/s, achieves a Nyquist SNDR of 65 dB (10.55 ENOB) and an SFDR of 71 dB. Its INL and DNL are 0.68 LSB and 0.66 LSB, respectively
ORBIT: Hybrid movie recommendation engine

Today, users will get so many movie recommendations websites, which suggest users best movies according to their interests. All these websites have implemented one of the conventional content, context and collaborative recommendations algorithms. Alone, these algorithms are failed to recommend best and efficient recommendations to user. So, there is a need to evolve a unique algorithm which combines the features of conventional algorithm along with its new features. This paper describes the ORBIT, which is a movie recommendation engine, based on a unique Hybrid recommendation algorithm, satisfies a user by providing best and efficient books recommendations. Comparative case study of conventional recommendation algorithms to ORBIT's Hybrid movie recommendation algorithm has also been studied and presented in this paper. This case study is based on evaluating criteria of recommendation algorithm i.e. accuracy, precision, recall, F-measure etc. Results of this case study are represented in the form of tables and graphs to clearly specify the need of ORBIT.
Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we explore LSTM RNN architectures for large scale acoustic modeling in speech recognition. We recently showed that LSTM RNNs are more effective than DNNs and conventional RNNs for acoustic modeling, considering moderately-sized models trained on a single machine. Here, we introduce the first distributed training of LSTM RNNs using asynchronous stochastic gradient descent optimization on a large cluster of machines. We show that a two-layer deep LSTM RNN where each LSTM layer has a linear recurrent projection layer can exceed state-of-the-art speech recognition performance. This architecture makes more effective use of model parameters than the others considered, converges quickly, and outperforms a deep feed forward neural network having an order of magnitude more parameters.
Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique

This paper exhibits two methods for decreasing the time associated with training a machine learning classifier on biometric signals. Using electroencephalography (EEG) data obtained from a consumer-grade headset with a single electrode, we show that these methods produce significant gains in the computational performance and calibration time of a simple brain-computer interface (BCI) without significantly decreasing accuracy. We discuss the relevance of reduced feature vector size to the design of physiological computing applications.
Performance Analysis of LTE-Advanced Physical Layer

LTE-Advanced (LTE-A) is the project name of the evolved version of LTE that is being developed by 3GPP. LTE-A will meet or exceed the requirements of the International Telecommunication Union (ITU) for the Fourth Generation (4G) radio communication standard known as IMT-Advanced. LTE-A is being specified initially as part of Release 10 of the 3GPP specifications. In this paper, we present an in-depth analysis of the LTE-A downlink physical layer characteristics and its performance. Our work is unique in providing a detailed performance study based on Release 10 of the 3GPP standard. As performance metrics, Bit Error Rate (BER) and date throughput are evaluated in terms of Signal to Noise Ratio (SNR) for two different Multi-Input Multi-Output (MIMO) schemes as defined in LTE standard under different combination of digital modulation schemes. This study consider a first step to perform hardware implementation of LTE-A system using Field Programmable Gate Array (FPGA) technology that become a very important task for mobile communication and wireless network researchers.
Satellite remote sensing of surface air quality

Satellite remote sensing of air quality has evolved dramatically over the last decade. Global observations are now available for a wide range of species including aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, and SO2. Capabilities for satellite remote sensing of these species in the boundary layer are reviewed for current instruments, along with physical processes affecting their accuracy and precision. Applications of satellite observations are discussed for case studies of specific events, for estimates of surface concentrations, and to improve emission inventories of trace gases and aerosols. Aerosol remote sensing at visible wavelengths exhibits high sensitivity to boundary layer concentrations. Although atmospheric scattering and surface emission of thermal radiation generally reduce instrument sensitivity to trace gases near the surface, a strong boundary layer signal in NO2 arises from its large boundary layer concentrations relative to the free troposphere. Recommendations are presented including (1) additional dedicated validation activities, especially for tropospheric NO2 and HCHO; (2) improved characterization of geophysical fields that affect remote sensing of trace gases and aerosols; (3) continued development of comprehensive assimilation and inversion capabilities to relate satellite observations to emissions and surface concentrations; (4) development of satellite instruments and algorithms to achieve higher spatial resolution to resolve urban scales, facilitate validation, and reduce cloud contamination that increases remote sensing error; and (5) support for the next generate of satellite instrumentation designed for air quality applications.
Coping with stress: divergent strategies of optimists and pessimists.

Previous research has shown that dispositional optimism is a prospective predictor of successful adaptation to stressful encounters. In this research we attempted to identify possible mechanisms underlying these effects by examining how optimists differ from pessimists in the kinds of coping strategies that they use. The results of two separate studies revealed modest but reliable positive correlations between optimism and problem-focused coping, seeking of social support, and emphasizing positive aspects of the stressful situation. Pessimism was associated with denial and distancing (Study 1), with focusing on stressful feelings, and with disengagement from the goal with which the stressor was interfering (Study 2). Study 1 also found a positive association between optimism and acceptance/resignation, but only when the event was construed as uncontrollable. Discussion centers on the implications of these findings for understanding the meaning of people's coping efforts in stressful circumstances.
Electroencephalography (EEG) Based Mobile Robot Control through an Adaptive Brain Robot Interface

This project mentioned a couple of brain controlled automaton supported Brain–computer interfaces (BCI). BCIs square measure systems that may by pass standard channels of communication (i.e., muscles and thoughts) to produce direct communication and management between the human brain and physical devices by translating totally different patterns of brain activity into commands in real time. With these commands mobile automaton may be controlled. The intention of the project work is to develop automaton that may assist the disabled folks in their standard of living to try and do some work freelance on others. Brain signals are detected by the brain wave device and it'll convert the info into packets and transmit through Bluetooth medium. Level instrument unit (LAU) can receive the brain wave information and it'll extract and method the signal victimization Mat lab platform. Then the management commands are transmitted to the robotic ARM module to method. With this whole system, we will choose AN object and place it consequently through the designed brain signals.
Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder the performance of collaborative filtering-based methods. Moreover, user preferences may vary dramatically with respect to the geographical regions due to different urban compositions and cultures. To address these challenges, we stand on recent advances in deep learning and propose a Spatial-Aware Hierarchical Collaborative Deep Learning model (SH-CDL). The model jointly performs deep representation learning for POIs from heterogeneous features and hierarchically additive representation learning for spatial-aware personal preferences. To combat data sparsity in spatial-aware user preference modeling, both the collective preferences of the public in a given target region and the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the POIs, we introduce a late feature fusion strategy into our SH-CDL model. The extensive experimental analysis shows that our proposed model outperforms the state-of-the-art recommendation models, especially in out-of-town and cold-start recommendation scenarios.
Recommending and Evaluating Choices in a Virtual Community of Use

When making a choice in the absence of decisive first-hand knowledge, choosing as other like-minded, similarly-situated people have successfully chosen in the past is a good strategy — in effect, using other people as filters and guides: filters to strain out potentially bad choices and guides to point out potentially good choices. Current human-computer interfaces largely ignore the power of the social strategy. For most choices within an interface, new users are left to fend for themselves and if necessary, to pursue help outside of the interface. We present a general history-of-use method that automates a social method for informing choice and report on how it fares in the context of a fielded test case: the selection of videos from a large set. The positive results show that communal history-of-use data can serve as a powerful resource for use in interfaces.
Power-constrained CMOS scaling limits

The scaling of CMOS technology has progressed rapidly for three decades, but may soon come to an end because of powerdissipation constraints. The primary problem is static power dissipation, which is caused by leakage currents arising from quantum tunneling and thermal excitations. The details of these effects, along with other scaling issues, are discussed in the context of their dependence on application. On the basis of these considerations, the limits of CMOS scaling are estimated for various application scenarios.
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies

Now, we come to offer you the right catalogues of book to open. bio inspired artificial intelligence theories methods and technologies intelligent robotics and is one of the literary work in this world in suitable to be reading material. That's not only this book gives reference, but also it will show you the amazing benefits of reading a book. Developing your countless minds is needed; moreover you are kind of people with great curiosity. So, the book is very appropriate for you.
A Document Skew Detection Method Using the Hough Transform

Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flatbed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realising a practical document reader. In this paper we describe a new algorithm for skew detection. We then compare the performance and results of this skew detection algorithm to other publidhed methods form O'Gorman, Hinds, Le, Baird, Posel and Akuyama. Finally, we discuss the theory of skew detection and the different apporaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to be extremenly fast, with run times averaging under 0.25 CPU seconds to calculate the angle on the DEC 5000/20 workstation.
Multi-Task Pose-Invariant Face Recognition

Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This paper proposes a novel face identification framework capable of handling the full range of pose variations within ±90° of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multi-task learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Finally, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top-level performance on the challenging LFW data set.