nils-herrmann/clean_scirepeval huggingface dataset:
High efficiency transformerless MOSFET inverter for grid-tied photovoltaic system

This paper proposes a new high efficiency single phase transformerless grid-tied photovoltaic (PV) inverter by using super-junction MOSFETs as main power switches. No reverse recovery issues are required for the main power switches and the blocking voltages across the switches are half of the DC input voltage in the proposed topology. Therefore, the super-junction MOSFETs have been used to improve the efficiency. Two additional switches with the conventional full H-bridge topology, make sure the disconnection of PV module from the grid at the freewheeling mode. As a result, the high frequency common mode (CM) voltage which leads leakage current is minimized. PWM dead time is not necessary for the proposed topology which reduces the distortion of the AC output current. The efficiency at light load is increased by using MOSFET as main power switches which increases the European Union (EU) efficiency of the proposed topology. The proposed inverter can also operate with high frequency by retaining high efficiency which enables reduced cooling system. The total semiconductor device losses for the proposed topology and several existing topologies are calculated and compared. Finally, the proposed new topology is simulated by MATLAB/Simulink software to validate the accuracy of the theoretical explanation. It is being manufactured to verify the experimental results.
Modeling and Understanding Human Routine Behavior

Human routines are blueprints of behavior, which allow people to accomplish purposeful repetitive tasks at many levels, ranging from the structure of their day to how they drive through an intersection. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. However, existing routine models do not capture the causal relationships between situations and actions that describe routines. Our main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines. We apply this algorithm on two example datasets, and show that the modeled routines are meaningful-that they are predictive of people's actions and that the modeled causal relationships provide insights about the routines that match findings from previous research. Our approach offers a generalizable solution to model and reason about routines.
The Design of Wideband Phased Array Antenna using Ridged Tapered Slot Antenna

In this paper, instead of multilayered notch antenna, the ridged tapered slot antenna (RTSA) is chosen as an element of wideband phased array antenna (PAA) since it has rigid body and can be easily manufactured by mechanical wire-cutting. In addition, because the RTSA is made of conductor, it doesn't need via-holes which are required to avoid the blind angles out of the operation frequency band. Theses blind angles come from the self resonance of the dielectric material of notch antenna. We developed wide band/wide scan PAA which has a bandwidth of 3:1 and scan volume of plusmn45deg. In order to determine the shape of the RTSA, the active VSWR (AVSWR) of the RTSA was optimized in the numerical waveguide simulator. And then using the E-plane/H-plane simulator, the AVSWR with beam scan angles in E-plane/H-plane are calculated respectively. On the basis of optimized design, numerical analysis of finite arrays was performed by commercial time domain solver. Through the simulation of 10 times 6 quad-element RTSA arrays, the AVSWR at the center element was computed and compared with the measured result. The active element pattern (AEP) of 10 times 6 quad-element RTSA arrays was also computed and had a good agreement with the measured AEP. From the result of the AEP, we can easily predict that 10 times 6 quad-element RTSA arrays have a good beam scanning capabilities
Reinforcement Learning of Local Shape in the Game of Go

We explore an application to the game of Go of a reinforcement learning approach based on a linear evaluation function and large numbers of binary features. This strategy has proved effective in game playing programs and other reinforcement learning applications. We apply this strategy to Go by creating over a million features based on templates for small fragments of the board, and then use temporal difference learning and self-play. This method identifies hundreds of low level shapes with recognisable significance to expert Go players, and provides quantitive estimates of their values. We analyse the relative contributions to performance of templates of different types and sizes. Our results show that small, translation-invariant templates are surprisingly effective. We assess the performance of our program by playing against the Average Liberty Player and a variety of computer opponents on the 9×9Computer Go Server. Our linear evaluation function appears to outperform all other static evaluation functions that do not incorporate substantial domain knowledge.
Rethinking procrastination: positive effects of "active" procrastination behavior on attitudes and performance.

Researchers and practitioners have long regarded procrastination as a self-handicapping and dysfunctional behavior. In the present study, the authors proposed that not all procrastination behaviors either are harmful or lead to negative consequences. Specifically, the authors differentiated two types of procrastinators: passive procrastinators versus active procrastinators. Passive procrastinators are procrastinators in the traditional sense. They are paralyzed by their indecision to act and fail to complete tasks on time. In contrast, active procrastinators are a "positive" type of procrastinator. They prefer to work under pressure, and they make deliberate decisions to procrastinate. The present results showed that although active procrastinators procrastinate to the same degree as passive procrastinators, they are more similar to nonprocrastinators than to passive procrastinators in terms of purposive use of time, control of time, self-efficacy belief, coping styles, and outcomes including academic performance. The present findings offer a more sophisticated understanding of procrastination behavior and indicate a need to reevaluate its implications for outcomes of individuals.
A lightweight active service migration framework for computational offloading in mobile cloud computing

Cloud computing enables access to the widespread services and resources in cloud datacenters for mitigating resource limitations in low-potential client devices. Computational cloud is an attractive platform for computational offloading due to the attributes of scalability and availability of resources. Therefore, mobile cloud computing (MCC) leverages the application processing services of computational clouds for enabling computational-intensive and ubiquitous mobile applications on smart mobile devices (SMDs). Computational offloading frameworks focus on offloading intensive mobile applications at different granularity levels which involve resource-intensive mechanism of application profiling and partitioning at runtime. As a result, the energy consumption cost (ECC) and turnaround time of the application is increased. This paper proposes an active service migration (ASM) framework for computational offloading to cloud datacenters, which employs lightweight procedure for the deployment of runtime distributed platform. The proposed framework employs coarse granularity level and simple developmental and deployment procedures for computational offloading in MCC. ASM is evaluated by benchmarking prototype application on the Android devices in the real MCC environment. It is found that the turnaround time of the application reduces up to 45 % and ECC of the application reduces up to 33 % in ASM-based computational offloading as compared to traditional offloading techniques which shows the lightweight nature of the proposed framework for computational offloading.
A cyber forensics needs analysis survey: Revisiting the domain's needs a decade later

The number of successful cyber attacks continues to increase, threatening financial and personal security worldwide. Cyber/digital forensics is undergoing a paradigm shift in which evidence is frequently massive in size, demands live acquisition, and may be insufficient to convict a criminal residing in another legal jurisdiction. This paper presents the findings of the first broad needs analysis survey in cyber forensics in nearly a decade, aimed at obtaining an updated consensus of professional attitudes in order to optimize resource allocation and to prioritize problems and possible solutions more efficiently. Results from the 99 respondents gave compelling testimony that the following will be necessary in the future: (1) better education/training/certification (opportunities, standardization, and skill-sets); (2) support for cloud and mobile forensics; (3) backing for and improvement of open-source tools (4) research on encryption, malware, and trail obfuscation; (5) revised laws (specific, up-to-date, and which protect user privacy); (6) better communication, especially between/with law enforcement (including establishing new frameworks to mitigate problematic communication); (7) more personnel and funding.
Sleep patterns of college students at a public university.

The authors' purpose in this study was to determine the sleep patterns of college students to identify problem areas and potential solutions.



A total of 313 students returned completed surveys.



A sleep survey was e-mailed to a random sample of students at a North Central university. Questions included individual sleep patterns, problems, and possible influencing factors.



Most students reported later bedtimes and rise times on weekends than they did on weekdays. More than 33% of the students took longer than 30 minutes to fall asleep, and 43% woke more than once nightly. More than 33% reported being tired during the day. The authors found no differences between freshmen, sophomores, juniors, seniors, and graduate students for time to fall asleep, number of night wakings, or total time slept each night.



Many students have sleep problems that may interfere with daily performance, such as driving and academics. Circadian rhythm management, sleep hygiene, and white noise could ameliorate sleep difficulties.
High-Efficiency Bridgeless Flyback Rectifier With Bidirectional Switch and Dual Output Windings

This paper proposes and analyzes a new bridgeless flyback power factor correction rectifier for ac-dc power conversion. By eliminating four bridge diodes and adding a few circuit elements, the proposed rectifier reduces the primary side conduction loss and improves efficiency. The addition of the new elements has minimal effect on the circuit simplicity because it does not need any additional gate driver and magnetic elements. The losses in the semiconductor devices of the proposed circuit are analyzed and compared with that of the conventional one, followed by transformer design guideline. Experimental results with the practically implemented prototype prove its higher efficiency than its conventional counterparts.
Non-Negative Residual Matrix Factorization with Application to Graph Anomaly Detection

Given an IP source-destination traffic network, how do we spot mis-behavioral IP sources (e.g., port-scanner)? How do we find strange users in a user-movie rating graph? Moreover, how can we present the results intuitively so that it is relatively easier for data analysts to interpret? We proposeNrMF, a non-negative residual matrix factorization framework, to address such challenges. We present an optimization formulation as well as an effective algorithm to solve it. Our method can naturally capture abnormal behaviors on graphs. In addition, the proposed algorithm is linear wrt the size of the graph therefore it is suita ble for large graphs. The experimental results on several data sets validate its effectiveness as well as efficiency.
AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers

Energy costs for data centers continue to rise, already exceeding $15 billion yearly. Sadly much of this power is wasted. Servers are only busy 10--30% of the time on average, but they are often left on, while idle, utilizing 60% or more of peak power when in the idle state.
 We introduce a dynamic capacity management policy, AutoScale, that greatly reduces the number of servers needed in data centers driven by unpredictable, time-varying load, while meeting response time SLAs. AutoScale scales the data center capacity, adding or removing servers as needed. AutoScale has two key features: (i) it autonomically maintains just the right amount of spare capacity to handle bursts in the request rate; and (ii) it is robust not just to changes in the request rate of real-world traces, but also request size and server efficiency.
 We evaluate our dynamic capacity management approach via implementation on a 38-server multi-tier data center, serving a web site of the type seen in Facebook or Amazon, with a key-value store workload. We demonstrate that AutoScale vastly improves upon existing dynamic capacity management policies with respect to meeting SLAs and robustness.
FASHION MARKETING IN SOCIAL MEDIA : USING INSTAGRAM FOR FASHION BRANDING

Social media has become an important tool for the business of marketers. Increasing exposure and traffics are the main two benefits of social media marketing. Most marketers are using social media to develop loyal fans and gain marketplace intelligence. Marketers reported increased benefits across all categories since 2013 and trademarks increased the number of loyal fans and sales . Therefore, 2013 was a significant year for social media. Feeling the power of Instagram may be one of the most interesting cases. Social media is an effective key for fashion brands as they allow them to communicate directly with their consumers, promote various events and initiatives, and build brand awareness. As the increasing use of visual info graphic and marketing practices in social media, trademarks has begun to show more interest in Instagram. There is also no language barriers in Instagram and provides visuals which are very crucial for fashion industry. The purpose of this study is to determine and contrast the content sharing types of 10 well-known fashion brands (5 Turkish brands and 5 international brands), and to explain their attitude in Instagram. Hence, the content of Instagram accounts of those brands were examined according to post type (photo/video), content type (9 elements), number of likes and reviews, photo type (amateur/professional), shooting place (studio/outdoor/shops/etc.), and brand comments on their posts. This study provides a snapshot of how fashion brands utilize Instagram in their efforts of marketing.
Differentiable programming for image processing and deep learning in halide

Gradient-based optimization has enabled dramatic advances in computational imaging through techniques like deep learning and nonlinear optimization. These methods require gradients not just of simple mathematical functions, but of general programs which encode complex transformations of images and graphical data. Unfortunately, practitioners have traditionally been limited to either hand-deriving gradients of complex computations, or composing programs from a limited set of coarse-grained operators in deep learning frameworks. At the same time, writing programs with the level of performance needed for imaging and deep learning is prohibitively difficult for most programmers.
 We extend the image processing language Halide with general reverse-mode automatic differentiation (AD), and the ability to automatically optimize the implementation of gradient computations. This enables automatic computation of the gradients of arbitrary Halide programs, at high performance, with little programmer effort. A key challenge is to structure the gradient code to retain parallelism. We define a simple algorithm to automatically schedule these pipelines, and show how Halide's existing scheduling primitives can express and extend the key AD optimization of "checkpointing."
 Using this new tool, we show how to easily define new neural network layers which automatically compile to high-performance GPU implementations, and how to solve nonlinear inverse problems from computational imaging. Finally, we show how differentiable programming enables dramatically improving the quality of even traditional, feed-forward image processing algorithms, blurring the distinction between classical and deep methods.
Unsupervised method for sentiment analysis in online texts

In recent years, the explosive growth of online media, such as blogs and social networking sites, has enabled individuals and organizations to write about their personal experiences and express opinions. Classifying these documents using a polarity metric is an arduous task. We propose a novel approach to predicting sentiment in online textual messages such as tweets and reviews, based on an unsupervised dependency parsing-based text classification method that leverages a variety of natural language processing techniques and sentiment features primarily derived from sentiment lexicons. These lexicons were created by means of a semiautomatic polarity expansion algorithm in order to improve accuracy in specific application domains. The results obtained for the Cornell Movie Review, Obama-McCain Debate and SemEval-2015 datasets confirm the competitive performance and the robustness of the system.
Event-related cortical desynchronization detected by power measurements of scalp EEG.

Changes in the background EEG activity occurring at the same time as visual and auditory evoked potentials, as well as during the interstimulus interval in a CNV paradigm were analysed in human subjects, using serial power measurements of overlapping EEG segments. The analysis was focused on the power of the rhythmic activity within the alpha band (RAAB power). A decrease in RAAB power occurring during these event-related phenomena was indicative of desynchronization. Phasic, i.e. short lasting, localised desynchronization was present during sensory stimulation, and also preceding the imperative signal and motor response (motor preactivation) in the CNV paradigm.
NoSQL vs relational database: A comparative study about the generation of the most frequent N-grams

This study intends to help data mining developers to get better performance when obtaining the most frequent N-grams in Text Mining projects. The process of building new variables is one of the oldest and still challenging problems in Data Mining projects. The most frequent N-grams are commonly used as input variables in Text Mining projects. The N-grams represent the occurrence of N items in sequence in a given text. The items can be letters or words. This paper presents a performance comparison between the two main approaches of data storage, relational and NoSQL databases in the task of obtaining the most frequent N-grams. Validation of the study was executed using a database from a known benchmark from an international competition organized by PAN@CLEF 2013. The one-tailed paired t-test showed that NoSQL approach is statistically superior to the relational approach with a confidence level of 95%.
Semantic reconciliation of knowledge extracted from text through a novel machine reader

This paper describes a novel method for generating and integrating knowledge graphs extracted from multiple natural language sources by FRED, a machine reading tool for generating abstract representations of text documents. This is a key problem in human-robot spoken dialogue interaction, issue which arises from a current research project related to active and healthy ageing using caring service robots where we are involved. The problem is also relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, such as automatic news summarisation. Solving this problem requires solving sub-tasks that have only been studied individually, so far. We propose a holistic approach to handle FRED's graphs related to different input texts and output a knowledge graph representing the reconciled knowledge.
Congestion-aware edge caching for adaptive video streaming in Information-Centric Networks

This paper proposes a network-aware resource management scheme that improves the quality of experience (QoE) for adaptive video streaming in CDNs and Information-Centric Networks (ICN) in general, and Dynamic Adaptive Streaming over HTTP (DASH) in particular. By utilizing the DASH manifest, the network (by way of a logically centralized controller) computes the available link resources and schedules the chunk dissemination to edge caches ahead of the end-user's requests. Our approach is optimized for multi-rate DASH videos. We implemented our resource management scheme, and demonstrated that in the scenario when network conditions evolve quickly, our approach can maintain smooth high quality playback. We show on actual video server data and in our own simulation environment that a significant reduction in peak bandwidth of 20% can be achieved using our approach.
Accurate 3D face reconstruction from weakly calibrated wide baseline images with profile contours

We propose a method to generate a highly accurate 3D face model from a set of wide-baseline images in a weakly calibrated setup. Our approach is purely data driven, and produces faithful 3D models without any pre-defined models, unlike other statistical model-based approaches. Our results do not rely upon a critical initialization step nor parameters for optimization steps. We process 5 images (including profile views), infer the accurate poses of cameras in all views, and then infer a dense 3D face model. The quality of 3D face models depends on the accuracy of estimated head-camera motion. First, we propose to use an iterative bundle adjustment approach to remove outliers in corresponding points. Contours in the profile views are matched to provide reliable correspondences that link two opposite side of views together. For dense reconstruction, we propose to use a face-specific cylindrical representation which allows us to solve a global optimization problem for N-view dense aggregation. Profile contours are used once again to provide constraints in the optimization step. Experimental results using synthetic and real images show that our method provides accurate and stable reconstruction results on wide-baseline images. We compare our method with state of the art methods, and show that it provides significantly better results in terms of both accuracy and efficiency.
Network defense: Approaches, methods and techniques

To defend a network from intrusion is a generic problem of all time. It is important to develop a defense mechanism to secure the network from anomalous activities. This paper presents a comprehensive survey of methods and systems introduced by researchers in the past two decades to protect network resources from intrusion. A detailed pros and cons analysis of these methods and systems is also reported in this paper. Further, this paper also provides a list of issues and research challenges in this evolving field of research. We believe that, this knowledge will help to create a defense system.