nils-herrmann/clean_scirepeval huggingface dataset:
Ultrawide-band transitions and new microwave components using double-sided parallel-strip lines

Double-sided parallel-strip lines have been used as a balanced transmission line to feed double-sided printed antennas or to fabricate microwave components such as balanced mixers. In this paper, ultrawide-band transitions from microstrip line to double-sided parallel-strip line are developed with 1-30-GHz bandwidth. New microwave components using double-sided parallel-strip lines are designed with good measured results. The double-sided parallel-strip line has an advantage of easy realization of low-impedance lines, which are important for many component designs.
Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety

This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore and predict the role of road users on possible injury risks. The research employed Classification and Adaptive Regression Trees (CART) and RandomForest approaches. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to many sided analyses. Empirical results showed that the models could classify accidents with promising accuracy.
Programming languages and dimensions

Scientists and engineers must ensure that the equations and formulae which they use are dimensionally consistent, but existing programming languages treat all numeric values as dimensionless. This thesis investigates the extension of programming languages to support the notion of physical dimension. A type system is presented similar to that of the programming language ML but extended with polymorphic dimension types. An algorithm which infers most general dimension types automatically is then described and proved correct. The semantics of the language is given by a translation into an explicitlytyped language in which dimensions are passed as arguments to functions. The operational semantics of this language is specified in the usual way by an evaluation relation defined by a set of rules. This is used to show that if a program is well-typed then no dimension errors can occur during its evaluation. More abstract properties of the language are investigated using a denotational semantics: these include a notion of invariance under changes in the units of measure used, analogous to parametricity in the polymorphic lambda calculus. Finally the dissertation is summarised and many possible directions for future research in dimension types and related type systems are described.
ESD protection design to overcome internal damage on interface circuits of a CMOS IC with multiple separated power pins

This paper reports a real case of electrostatic discharge (ESD) improvement on a complementary metal oxide semiconductor integrated circuit (IC) product with multiple separated power pins. After ESD stresses, the internal damage have been found to locate at the interface circuit connecting between different circuit blocks with different power supplies. Some ESD designs have been implemented to rescue this IC product to meet the required ESD specification. By adding only an extra ESD clamp N-channel metal oxide semiconductor with a channel width of 10 /spl mu/m between the interface node and the ground line, the human-body-model (HBM) ESD level of this IC product can be improved from the original 0.5 to 3 kV. By connecting the separated vertical sync signal (VSS) power lines through the ESD conduction circuit to a common VSS ESD bus realized by the seal ring, the HBM ESD level of the enhanced version IC product with 12 separated power supplies pairs can be significantly improved from original 1 kV up to > 5 kV, without the noise coupling issue.
Predicting Amazon review helpfulness [ CSE 255 Assignment 1 ]

Reviews on amazon are ranked by how helpful they are rated by users in an effort to quickly summarize the opinions of a product for potential buyers. This project aims to explore what factors affect a review’s helpfulness by building a classification model on the Amazon movie reviews data set. The model performs well with accuracies over 85% and it is found that a review’s writing style, product rating and unigram features affect helpfulness the most.
Influence and correlation in social networks

In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.
 This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.
 In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.
 We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.
Analysis and Opinion mining using SentiwordNet : A Survey

With the advancement of technology and internet, there is abundant information available for the user on the internet. These users use this information and also provide their valuable feedback, suggestions, views on various review websites, blogs and social networks. For a better decision making it’s very essential to analyze, explore and organize their views and feedback. This can be done with the help of opinion mining or sentiment analysis. Sentiment analysis or the opinion mining is a task aimed at extraction and analysis of the data in form of positive, negative or neutral to provide decision making. The sentiment analysis can be done in many ways one of these is with the use of lexicon. This survey gives an overview of the efficient techniques to be used, some recent advancements in the techniques and the future research directions in the field of Sentiment Analysis.
VabCut: A video extension of GrabCut for unsupervised video foreground object segmentation

This paper introduces VabCut, a video extension of GrabCut, an original unsupervised solution to tackle the video foreground object segmentation task. Vabcut works on an extension of the RGB colour domain to RGBM, where M is the motion. It requires a prior step: the computation of the motion layer (M-layer) of the frame to segment. In order to compute this layer we propose to intersect the frame to segment with N temporally close aligned frames. This paper also introduces a new iterative and collaborative method for an optimal frame alignment, based on points of interest and RANSAC, which automatically discards outliers and refines the homographies in turns. The whole method is fully automatic and can handle standard video, i.e. not professional, shaky, blurry or else. We tested VabCut on the SegTrack 2011 benchmark, and demonstrated its effectiveness, it especially outperforms the state of the art methods while being faster.
Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the “coding criterion” to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than “shallow” methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.
Programming Online Judge System

With the development of online education, the pattern of education which based on the Internet obtained the general recognition of the society. People prefer to learn and share the open courses on the Internet in universities or colleges all over the world. However, the courses opened online can support the massive peoples to learn online, but couldn't support they to program on the network, and judge the program on the network. In order to support more and more students to complete the experiments of the online courses, automatic judging system about software experiments and hardware experiments are needed to develop to ensure the correctness of the design of experiments.  Evaluation System is in MOOC (massive online open courses) environment, it can connect different experiment systems to finish different courses. It also can simulate the hardware experiments online and test hardware experiments automatically. Programming online judge system, mentioned in this paper, can solve much of the problems. With the help of Programming online judge system, different universities can share their resources and save investment. Users can look up for the information conveniently and get experience similar to local experiment.
Profiler: integrated statistical analysis and visualization for data quality assessment

Data quality issues such as missing, erroneous, extreme and duplicate values undermine analysis and are time-consuming to find and fix. Automated methods can help identify anomalies, but determining what constitutes an error is context-dependent and so requires human judgment. While visualization tools can facilitate this process, analysts must often manually construct the necessary views, requiring significant expertise. We present Profiler, a visual analysis tool for assessing quality issues in tabular data. Profiler applies data mining methods to automatically flag problematic data and suggests coordinated summary visualizations for assessing the data in context. The system contributes novel methods for integrated statistical and visual analysis, automatic view suggestion, and scalable visual summaries that support real-time interaction with millions of data points. We present Profiler's architecture --- including modular components for custom data types, anomaly detection routines and summary visualizations --- and describe its application to motion picture, natural disaster and water quality data sets.
A POMDP Formulation of Preference Elicitation Problems

Preference elicitation is a key problem facing the deployment of intelligent systems that make or recommend decisions on the behalf of users. Since not all aspects of a utility function have the same impact on object-level decision quality, determining which information to extract from a user is itself a sequential decision problem, balancing the amount of elicitation effort and time with decision quality. We formulate this problem as a partially-observable Markov decision process (POMDP). Because of the continuous nature of the state and action spaces of this POMDP, standard techniques cannot be used to solve it. We describe methods that exploit the special structure of preference elicitation to deal with parameterized belief states over the continuous state space, and gradient techniques for optimizing parameterized actions. These methods can be used with a number of different belief state representations, including mixture models.
A 60 GHz same-channel full-duplex CMOS transceiver and link based on reconfigurable polarization-based antenna cancellation

This paper describes a direct-conversion 45nm SOI CMOS 60 GHz transceiver for same-channel full duplex applications. A novel polarization-based wideband self-interference cancellation (SIC) technique in the antenna domain is described that can be reconfigured from the IC. In order to achieve the high levels of required SIC, a second RF cancellation path from the transmitter output to the LNA output with >30 dB gain control and >360° phase control is also integrated. The TX and RX share the same LO to reduce the impact of phase noise on SIC. Antenna and RF cancellation together enable >70 dB of total self-interference suppression even in the presence of nearby reflectors. In conjuction with digital SIC impemented in MATLAB, a same-channel full-duplex link is demonstrated over 0.7 m. To the best of our knowledge, this work demonstrates the first fully-integrated full-duplex transceiver front-end and mm-wave link. While not our focus, the transceiver also achieves state-of-the-art saturated output power of +15 dBm, peak TX efficiency of 15.3% and RX NF of 4 dB.
Clio: A Semi-Automatic Tool For Schema Mapping

We consider the integration requirements of modern data intensive applications including data warehousing, global information systems and electronic commerce. At the heart of these requirements lies the schema mapping problem in which a source (legacy) database must be mapped into a di erent, but xed, target schema. The goal of schema mapping is the discovery of a query or set of queries to map source databases into the new structure. We demonstrate Clio, a new semi-automated tool for creating schema mappings. Clio employs a mapping-by-example paradigm that relies on the use of value mappings describing how a value of a target attribute can be created from a set of values of source attributes.
Hybrid genetic algorithm and simulated annealing for two-dimensional non-guillotine rectangular packing problems

In this paper, genetic algorithm (GA) and simulated annealing (SA) with improved bottom left (BL) algorithm were applied to twodimensional non-guillotine rectangular packing problems. The performance and efficiency of these algorithms on several test problems [Hopper, E., Turton, B.C.H., 2000. An empirical investigation of meta-heuristic and heuristic algorithms for two-dimensional packing problem. ] were compared. These test problems consist of 17 and 29 individual rectangular pieces to place in main object limited with a size of 200 200 units. Both solution approaches were compared, based on the trim losses of the test problems. Also, the influences of the GA parameters (population sizes, mutation rates, crossover techniques) and of the SA parameters (cooling schedules, neighborhood moves, the number of inner loop, different temperature values) on the solution of these problems were examined. For considering all solutions of the test problems, the hybrid GA produces much better results than the hybrid SA. r 2006
Superior thermal conductivity of single-layer graphene.

We report the measurement of the thermal conductivity of a suspended single-layer graphene. The room temperature values of the thermal conductivity in the range approximately (4.84+/-0.44)x10(3) to (5.30+/-0.48)x10(3) W/mK were extracted for a single-layer graphene from the dependence of the Raman G peak frequency on the excitation laser power and independently measured G peak temperature coefficient. The extremely high value of the thermal conductivity suggests that graphene can outperform carbon nanotubes in heat conduction. The superb thermal conduction property of graphene is beneficial for the proposed electronic applications and establishes graphene as an excellent material for thermal management.
Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology

Process mining aims at discovering, monitoring, and improving business processes by extracting knowledge from event logs. In this respect, process mining can be applied only if there are proper event logs that are compatible with accepted standards, such as extensible event stream (XES). Unfortunately, in many real world set-ups, such event logs are not explicitly given, but instead are implicitly represented in legacy information systems. In this work, we exploit a framework and associated methodology for the extraction of XES event logs from relational data sources that we have recently introduced. Our approach is based on describing logs by means of suitable annotations of a conceptual model of the available data, and builds on the ontology-based data access (OBDA) paradigm for the actual log extraction. Making use of a realworld case study in the services domain, we compare our novel approach with a more traditional extract-transform-load based one, and are able to illustrate its added value. We also present a set of tools that we have developed and that support the OBDA-based log extraction framework. The tools are integrated as plugins of the ProM process mining suite.
Measurements and Simulations of the Convective Heat Transfer Coefficients on the End Windings of an Electrical Machine

The prediction of the thermal behavior of an electrical machine in the design process basically depends on the used boundary conditions such as the convective heat transfer coefficient. Due to the complicated shape of the end windings, the heat transfer coefficient has to be identified empirically by using thermopiles as heat flux sensors placed at certain positions on the end windings, because an analytical derivation is not feasible. This paper presents the results of the measured values of the heat transfer on the end windings of an electrical machine. The gained coefficients are compared to the results obtained by numerical simulations using commercial software for computational fluid dynamics. The simulation results are discussed according to the validity of the gained heat transfer coefficient. The configuration of the simulation has been tested on a cylindrical shape in a cross-flow.
Digital Forensics as a Service: A game changer

How is it that digital investigators are always busy and still never have enough time to actually dig deep into digital evidence? In this paper we will explore the current implementation of the digital forensic process and analyze factors that impact the efficiency of this process. Next we explain how in the Netherlands a Digital Forensics as a Service implementation reduced case backlogs and freed up digital investigators to help detectives better understand the digital material. a 2014 The Authors.
Effects of users' envy and shame on social comparison that occurs on social network services

In the context of the social network service environment, we explore how discrete emotions—envy and shame, in particular—may mediate the effects of social comparison on behavior intention and psychological responses. Based on the survey responses of 446 university students, the results suggest that social comparison to media figures correlates with a range of emotional responses as well as with behavioral intention and psychological responses. Envy maintained a significantly greater association with switch intention as a behavioral intention compared to shame. Conversely, shame was significantly related to burnout as a psychological response. Further, mediational analyses were consistent with the argument that envy and shame mediate the social comparison–outcome variables relationship. This research helps to illuminate the driving mechanism for the emotional effect that social comparison on social network service could elicit from a user. This predicts the nature of the behavioral and psychological outcome associated with the comparison and has implications for an enhanced understanding of the way in which the unique social network service communication environment may stimulate this process.
Learning to Navigate for Fine-Grained Classification

Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision mechanism to effectively localize informative regions without the need of bounding-box/part annotations. Our model, termed NTS-Net for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a Teacher agent and a Scrutinizer agent. In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher. After that, the Scrutinizer scrutinizes the proposed regions from Navigator and makes predictions. Our model can be viewed as a multi-agent cooperation, wherein agents benefit from each other, and make progress together. NTS-Net can be trained end-to-end, while provides accurate fine-grained classification predictions as well as highly informative regions during inference. We achieve state-of-the-art performance in extensive benchmark datasets.