Miracle-dz/UniFAR-FTUs huggingface dataset:
More is Better: Using Machine Learning Techniques and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

Attributing value to our surroundings and constructing preferences between alternatives is such an enormous part of human life, that it has long drawn researchers from various fields to attempt predicting individual preferences of consumer products and general population success of marketing campaigns. Traditional behavioral methods, such as interviews, focus groups, and questionnaires, have various limitations, calling for novel measurements in order to improve predictive power. In this study, we recorded the EEG responses of subjects, as they watched commercials of six different products. We explored various well-established measures of EEG and combined them in an attempt to predict subjects’ individual future preferences of products as obtained by a later binary choice task, above and beyond the predictive power of standard marketing questionnaires also filled by the subjects. In addition, we employ the same measures to predict the commercials’ population success, as measured both by YouTube metrics and by an online cohort of subjects. Our prediction models, which comprise of various machine learning algorithms, reached 67% accuracy in predicting between the most and least preferred items on a subject-by-subject level. Moreover, we were able to reach lower than chance RMSE score for predicting the rank order preferences of all six products. Importantly, we found EEG measures to be particularly successful, above and beyond but even more so than questionnaires, in predicting commercials’ population success. Our results show great promise for utilization of EEG measures by managers, marketing practitioners and researchers, as a valuable tool for market place predictions and managerial decisions.
NetPrints : Diagnosing Home Network Misconfigurations Using Shared Knowledge

Networks and networked applications depend on several pieces of configuration information to operate correctly. Such information resides in routers, firewalls, and end hosts, among other places. Incorrect information, ormisconfiguration, could interfere with the running of networked applications. This problem is particularly acute in consumer settings such as home networks, where there is a huge diversi ty of network elements and applications coupled with the absen ce of network administrators. To address this problem, we presentNetPrints, a system that leverages shared knowledge in a population of users to diagnose and resolve misconfigurations. Basically, if one u ser has figured out the fix for a problem, we would like this knowledge made available to another user experiencing the same problem. NetPrints records and aggregates configurati on information from a large population of clients, annotates it with compact network problem signatures, looks up the appropriate information when a new client experiences a similar problem, and suggests configuration changes to reso lve the problem. NetPrints performs all of these steps automatically, with little human involvement. We evaluate NetPrints in the context of several home networking problems actually reported by users, and find that it is effective in sifting through large volumes of shared configuration data to identify the relevant fix.
Spatial patterns of sediment connectivity in terraced lands: Anthropogenic controls of catchment sensitivity

In recent decades, the dynamics of global change in developed countries has led to significant alterations in the hydrological and sediment dynamics of terraced land. Agricultural terraces were built to control overland flow and prevent erosion, acting as buffers and barriers throughout the sediment cascading system. Their abandonment and degradation increase the sensitivity of the catchment, promoting the collapse of dry‐stone walls and the reworking of stored sediment. In this study, a geomorphometric index of connectivity—derived from high‐resolution LiDAR data (0.9 pt/m, RMSE < 0.2 m)—analysed the spatial patterns of structural connectivity in a small Mediterranean catchment (4.8 km2) characterized by a massive presence of terraces (37% of the surface area). The morphological characteristics of these anthropogenic features generated a dual effect: (a) general disconnectivity between different compartments of the catchment and (b) concentration of water and sediment flows along preferential pathways promoted by a cascade effect of collapse within the terraced areas. The fieldwork found that 73% of wall collapses were located on these pathways that showed high index of connectivity values (>Q8). This spatial matching was related to feedback dynamics between structural and functional connectivity, in which the failure of walls increases the concentration of runoff, which in turn accelerates the hydraulic processes causing their collapse. Identifying the most connected pathways within the most vulnerable structures in an integrated analysis could be a cost‐effective strategy for establishing priority areas for the management of terraced lands.
Journal of Digital Forensics, Journal of Digital Forensics, Security and Law Security and Law

Digital media (i.e. image, audio) has played an important role in today information system. The increasing of popularity in digital media has brought forth a number of technology advancements. This, however, also gives birth to a number of forgeries and attacks against this type of information. With the availability of easy-to-use media manipulating tools available online, the authenticity of to-day digital media cannot be guaranteed. In this paper, a new general framework for enhancing today media splicing detection has been proposed. By combining results from two traditional approaches, the enhanced detection results show improvement in term of clarity in which anomalies are more explicitly shown, providing easier and faster way for a forensic practitioner to investigate and verify the authenticity of the target digital media. Regarding the experiment, the developed framework is tested against a number of realistic tampered (spliced) media. Moreover, the enhanced detection results are compared with traditional approaches in order to ensure the eﬃciency of our proposed method in the realistic situation.
Modelling Vague Places with Knowledge from the Web

In this paper, we present a method for deriving an approximate spatial boundary for imprecise or vague geographical regions such the English Midlands. Geographic knowledge is crucial for any geographic information system (GIS) and often provided through gazetteers and geographical thesauri. However, these resources do not contain entries for vague regions even though common in natural language. We model spatial boundaries by deriving approximate polygon boundaries formed using geographic locations extracted from pages retrieved by searching the web. The method is evaluated using both precise and vague places and the approximate boundaries generated using this method can then be used within GIS, such as geographic IR systems. Results from using the web as a source of geographic knowledge for our approach are promising.
An Efficient Boosting Algorithm for Combining Preferences by

The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing RankBoost to nearest-neighbor and regression algorithms. Thesis Supervisor: David R. Karger Title: Associate Professor
An Eecient Boosting Algorithm for Combining Preferences

The problem of combining preferences arises in several applications, such as combining the results of diierent search engines. This work describes an eecient algorithm for combining multiple preferences. We rst give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an eecient implementation of the algorithm for a restricted case. We discuss two experiments we carried out to assess the performance of RankBoost. In the rst experiment, we used the algorithm to combine diierent WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-ltering task for making movie recommendations in which we present results comparing RankBoost to nearest-neighbor and regression algorithms.
Twentieth-century Pacific Decadal Oscillation simulated by CMIP5 coupled models

Abstract The authors examine the spatial and temporal characteristics of the simulated Pacific Decadal Oscillation (PDO) in 109 historical (i.e. all forcings) simulations derived from 25 coupled models within CMIP5. Compared with observations, most simulations successfully simulate the observed PDO pattern and its teleconnections to the SSTs in the tropical and southern Pacific. BNU-ESM, CanESM2, CCSM4, CESM1-FASTCHEM, FGOALS-g2, GFDL CM3, MIROC5, and NorESM1-M show better performance. Compared with the temporal phases of the observed PDO in the twentieth century, only five simulations — from CNRM-CM5, CSIRO Mk3.6. 0, HadCM3, and IPSL-CM5A-LR — simulate an evolution of the PDO similar to that derived from observation, which suggests that current coupled models can barely reproduce the observed phase shifting of the PDO. To capture characteristics of the observed PDO in the twentieth century, a requirement is that all the relevant external forcings are included in the models. How to add realistic oceanic initial states into the model may be another key point.
Programming by Predicates A formal model for interactive synthesis

Program synthesis is the problem of computing from a speciﬁcation a program that implements it. New and popular variations on the synthesis problem accept speciﬁcations in formats that are easier for the human synthesis user to provide: input-output example pairs, type information, and partial logical speciﬁcations. These are all partial speciﬁcation formats, encoding only a fraction of the expected behavior of the program, leaving many matching programs. This transition into partial speciﬁcation also changes the mode of work for the user, who now provides additional speciﬁcations until they are happy with the synthesis result. Therefore, synthesis becomes an iterative, interactive process. We present a formal model for interactive synthesis, parameterized by an abstract domain of predicates on programs. The abstract domain is used to describe both the iterative reﬁnement of the speciﬁcations and reduction of the candidate program space. We motivate the need for a general feedback model via predicates by showing that examples, the most frequent speciﬁcation tool, are an insuﬃcient tool to diﬀerentiate between programs, even when used as a full speciﬁcation. We use the formal model to describe the behavior of
Practical Data Science with R

Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.
Building Effects on theP‐Alert‐Based Real‐Time Shaking Map Determination

Recently, the P-wave-alert-device (P-alert) network, which is a dense array of microelectromechanical system (MEMS) accelerometers that was developed and installed by National Taiwan University for the purposes of earthquake early warnings, has recorded a large number of strong-motion records for moderate-to-large earthquakes throughout Taiwan. However, many of these stations are mounted on the vertical walls of buildings in ways such that further studies of the sensor-structure interactions on recorded acceleration data are required before the data is used in the production of high-quality shake maps. In this study, we collect the free-field accelerograms recorded by the Taiwan Strong-Motion Instrumentation Program (TSMIP) network that were operated by the Central Weather Bureau (CWB), where MEMS accelerometers were in the vicinity. Then, we compare the peak ground acceleration (PGA) ratio (R-value) between P-alert and TSMIP stations. Finally, we demonstrate how to use the R-value correction on the P-alert data, in order to rapidly produce high-resolution shake maps for relief work to be done soon after major earthquakes. At present, the shake maps produced by the P-alert network are posted automatically in real time on Facebook and are provided to the National Science and Technology Center for Disaster Reduction (NCDR) in order to allow for their relief work. These timely products provide improved information for disaster risk reduction, emergency preparedness, and emergency response.
Statistical clock skew analysis considering intradie-process variations

With shrinking cycle times, clock skew has become an increasingly difficult and important problem for high performance designs. Traditionally, clock skew has been analyzed using case-files which cannot model intradie-process variations and hence result in a very optimistic skew analysis. In this paper, we present a statistical skew analysis method to model intradie process variations. We first present a formal model of the statistical clock-skew problem and then propose an algorithm based on propagation of joint probability density functions in a bottom-up fashion in a clock tree. The analysis accounts for topological correlations between path delays and has linear runtime with the size of the clock tree. The proposed method was tested on several large clock-tree circuits, including a clock tree from a large industrial high-performance microprocessor. The results are compared with Monte Carlo simulation for accuracy comparison and demonstrate the need for statistical analysis of clock skew.
Identification of TCP Protocols

Adviser: Lisong Xu Recently, many new TCP algorithms, such as BIC, CUBIC, and CTCP, have been deployed in the Internet. Investigating the deployment statistics of these TCP algorithms is meaningful to study the performance and stability of the Internet. Currently, there is a tool named Congestion Avoidance Algorithm Identification (CAAI) for identifying the TCP algorithm of a web server and then for investigating the TCP deployment statistics. However, CAAI using a simple k-NN algorithm can not achieve a high identification accuracy. In this thesis, we comprehensively study the identification accuracy of five popular machine learning models. We find that the random forest model achieves the highest identification accuracy among these five models, and its identification accuracy is much higher than that of CAAI. iii ACKNOWLEDGMENTS I would like to express my appreciation to my advisory committee: to my advisor Dr. Lisong Xu for his expertise and patience. It is he who taught me how to find problems and solve problems which will benefit my whole life. We had many discussions on this work and he was always open, helpful and patient. This work would not have been done without his guidance. It is my honor to work in his group. Also I thank my co-advisor Dr. Dong Wang who gives me many suggestions to spark new ideas. I thank Professor Ying Lu and Professor Byrav Ramamurthy for reviewing my thesis and serving on my masters thesis defense committee. Furthermore, during this work, many people gave me good suggestions and technical help, I want to thank them here. I would like to thank Mr. Peng Yang who helps me in collecting the raw data and understanding the details of CAAI. His help greatly facilitates my work. I would like to thank all the people who always accompany me and support me. They are my families, professors, CSE staff, my friends and my classmates. My final words go to my parents who always support me and give me confidence.
A Comparative Study of Face Re-identification Systems under Real-World Conditions

Face re-identification is largely thought of as a solved problem in the research community, with State-ofthe-art systems attaining human-level performance on unconstrained image datasets. However, these results do not seem to translate to the real world. In systems matching people in surveillance-like footage to highquality images, reported performance is much lower than what the literature would suggest. In this work, we contribute a multi-modal dataset for evaluating real-world performance of facial re-identification systems. We then perform a verification and re-identification evaluation for state-of-art systems on both this dataset and the popular benchmarking dataset Labelled Faces in the Wild (LFW).
LDPC Decoder LLR Stopping Criterion

The log-likelihood ratio test on a single check node within the LDPC decoder is monitored to develop a stopping criterion for the decoder that is better than previous stopping criteria, without sacrificing the BER performance. Simulation results are presented for the transmission of the rate 1 2 (288, 576) WiMAX 802.16e LDPC code digits using binary phase shift keying (BPSK) over an AWGN channel.
Improving Performance through an Integrated Manufacturing Program

Every five to 10 years, a new manufacturing management program is introduced as the panacea for poor performance, although these programs have had mixed success. Many academics and practitioners believe that failures are due to the partial implementation of the programs and incompatible systems within the plant. Yet past research primarily considers manufacturing programs in isolation. In this article, the authors present a framework for integrating manufacturing programs, using the well-developed programs of total quality management (TQM), just-in-time (JIT), and total productive maintenance (TPM). These three programs are often considered components of the popular Lean manufacturing. The authors discuss the theoretical foundation for the positive impact of an integrated manufacturing program on manufacturing performance. They explore the theoretical relationships using structural equation modeling on a sample of 163 manufacturing plants. The authors' analysis provides evidence of the need for integrating the manufacturing practices. In their framework, together the practices of TQM, JIT, and TPM exhibit a consistent positive effect on multiple dimensions of manufacturing performance. The findings demonstrate the importance of implementing manufacturing practices that belong to all three programs and of integrating new programs with existing practices.
AIS Electronic

Technology-mediated learning refers to an environment in which the learner’s interactions with learning materials, peers, and/or instructors are mediated through information technologies (Alavi and Leidner, 2001). The objective of this paper is to review current research on technology-mediated learning on the basis of a theoretical framework derived from the existing literature. In the theoretical framework, three groups of determinants (human dimension, design dimension, and information technology) are presented that influence students’ psychological learning process, which eventually lead to different learning outcomes. The literature review shows that some of the relationships between information technology and other dimensions have been heavily examined (e.g., the influence of a technology feature on learning outcomes), while some others have been ignored (e.g., the influence of IT on student’s psychological process). Research questions that can help advance our understanding of technology-mediated learning are also presented and discussed.
MEERA: Cross-Layer Methodology for Energy Efficient Resource Allocation in Wireless Networks

In many portable devices, wireless network interfaces consume upwards of 30% of scarce system energy. Reducing the transceiver's power consumption to extend the system lifetime has therefore become a design goal. Our work is targeted at this goal and is based on the following two observations. First, conventional energy management approaches have focused independently on minimizing the fixed energy cost (by shutdown) and on scalable energy costs (by leveraging, for example, the modulation, code-rate and transmission power). These two energy management approaches present a tradeoff. For example, lower modulation rates and transmission power minimize the variable energy component, but this shortens the sleep duration thereby increasing fixed energy consumption. Second, in order to meet the quality of service (QoS) timeliness requirements for multiple users, we need to determine to what extent each system in the network may sleep and scale. Therefore, we propose a two-phase methodology that resolves the sleep-scaling tradeoff across the physical, communications and link layers at design time and schedules nodes at runtime with near optimal energy-efficient configurations in the solution space. As a result, we are able to achieve very low run-time overheads. Our methodology is applied to a case study on delivering a guaranteed QoS for multiple users with MPEG-4 video over a slow-fading channel. By exploiting runtime controllable parameters of actual RF components and a modified 802.11 medium access controller, system lifetime is increased by a factor of 3-to-10 in comparison with conventional techniques.