Miracle-dz/UniFAR-FTUs huggingface dataset:
Is conscious processing required for long-term memory?

Models of memory (including those of the SAM-REM variety) are generally based on the assumption that storage of information in long-term memory is a function of the amount of elaborative processing given to the processed information. Here we explore the limits of such an assumption by examining the long-term effects of information that has only been processed at a subliminal level. We conclude that even subliminally presented information is stored to some extent and will lead to small but consistent effects in indirect memory tests but not on direct memory tests. To have an effect on a direct memory test, the information has to be clearly seen (as in the RSVP experiments discussed in this chapter) although here again, attentive processing does not appear to be required.
The Relationship between BIBFRAME and OCLC's Linked-Data Model of Bibliographic Description: A Working Paper.

Updates: September 2013 — This version corrects a problem with attribution noted by reviewers of the previous draft. The models based on Schema.org described in Section 2 were derived from ideas being discussed by the W3C Schema Bib Extend community group but were developed by OCLC. The previous version of this draft incorrectly attributes this work to the W3C group. The analysis is otherwise unchanged. am also grateful for comments by Karen Coyle and other members of the W3C 'Schema Bib Extend' Community Group on an earlier version of this draft, which led to many improvements. But I am responsible for remaining inaccuracies.
UML-based domain ontology modeling for multi-agent system

In multi-agent application system, ontologies play an important role in facilitating the share and reuse of knowledge. In the field of object-oriented modeling, Unified Modeling Language (UML) has become standard modeling language and is widely supported by robust commercial tools. This paper shows how UML can be exploited to design the domain ontology model of multi-agent system at the agent level, in particular presents and ontology model diagram by extending the UML class diagram, and discusses the reasoning on UML-based ontology model.
Intelligent Cruise Control Field Operational Test

This interim document reports on a cooperative agreement between NHTSA and UMTRI entitled Intelligent Cruise Control (ICC) Field Operational Test (FOT). The overarching goal of the work is to characterize safety and comfort issues that are fundamental to human interactions with an automatic headway keeping system. This report summarizes the status of the FOT and presents preliminary results and findings deriving from the testing activities now in progress. It describes work done to prepare and instrument a fleet of 10 passenger cars with infrared ranging sensors, headway control algorithms, and driver interface units as needed to provide an adaptive cruise control (ACC) functionality. The vehicles have been given to lay drivers to use for two weeks as their personal cars. Based upon data from 35 drivers, objective and subjective results support the following preliminary observations: ACC driving is reported to be comfortable and is perceived as stress relieving. The kinesthetic sensation of ACC-induced deceleration was often cited by drivers as a vigilance-enhancing cue, perhaps implying a safety benefit. Drivers appear to learn how to use ACC quickly and to converge on a strategy that meshes with their driving style. The data contain a natural type of "bias" by which manual driving appears riskier than ACC driving in part because denser, more conflict-laden, traffic induces drivers to turn the ACC system off. Under virtually all conditions in which ACC is engaged, drivers choose (and the system provides) headway distances that are greater than those seen when the same person drives manually. ACC driving results in fewer "near approaches" to the preceding vehicle than does manual driving. Headway-keeping behavior differs markedly with driver age. Younger drivers are typically more aggressive, operating at shorter headways. Given the properties of the ACC system being studied, a minimal impact on the accident record would be expected from observations to date. A major element of this expectation derives from the drivers' choice to use ACC only in rather benign traffic environments. When completed, the FOT is expected to present findings based on results from over 100 drivers/participants.
Student-Made Video Projects in a Computer Technology Course

Communications skills are universally recognized as important within the education of engineers, however these skills are often underemphasized in engineering and technology courses. One new approach to emphasizing professional communications skills is the assignment of student made video projects. Some scholars argue that video has become the current vernacular of our culture. Video engages an audience in ways unique to the medium. Becoming fluent in communicating with video technology expands the versatility of future engineers and engineering technologists. Advances in digital video technologies have made these video projects within the reach of most undergraduate students, however many students have surprisingly little experience in this area. Outside of traditional media-making degree programs, few college students are required to create an edited video during their undergraduate experience. This paper explores the implementation of a student-made video project in a computer technology course. The action research described is grounded in a media literacy framework that uses a mixed methods approach. The video project explored has similar goals as a traditional undergraduate written research paper, only having a video replacing the paper as the finished product. Students must do research on a topic related to Internet and computer networking technologies and present their findings in a video format.
A Comparison of Clicker Effectiveness for Multiple Choice and Quantitative Questions

This article describes an empirical study of clicker effectiveness in an undergraduate operations management course. The study compares the performance of students in clicker sections with those in non-clicker sections on two types of exam questions, open-ended quantitative and multiple choice qualitative. Results show that the use of clickers during class lectures had a statistically significant impact on performance on open-ended quantitative exam questions, but no measurable impact on multiple-choice qualitative exam questions.
Synthesis of Pyrrolo[2,1-c][1,4]benzodiazepines via Reductive Cyclization of ω-Azido Carbonyl Compounds by TMSI: An Efficient Preparation of Antibiotic DC-81 and Its Dimers.

ω-Azido carbonyl compounds on reaction with trimethylsilyl iodide (in situ prepared from TMSCl/NaI) led to the formation of diazepine imines in good yields under mild conditions. This methodology has been applied to the parent unsubstituted pyrrolobenzodiazepine, the natural product DC-81 and its dimers.
Smile OAN: A Long Reach Hybrid WDM/TDM Passive Optical Network for Next Generation Optical Access

We present a hybrid WDM/TDM PON for next generation optical access, which supports 100 km reach, 640 Gbit/s capacity and 8000 ONUs. Commercially TDM-PONs can be smoothly migrated to the proposed network. The protection performance of the Smile OAN is also characterized.
Automatic Text Classification from Labeled and Unlabeled Data

Automatic text classification is a process that applies information retrieval technology and machine learning algorithms to build models from pre-labeled training samples and then deploys the models to previously unseen documents for classification. Text classification has been widely applied in many fields ranging from Web page indexing, document filtering, and information security, to business intelligence mining. This chapter presents a semi-supervised text classification framework that is based on the radial basis function (RBF) neural networks. The framework integrates an Expectation Maximization (EM) process into a RBF network and can learn for classification effectively from a very small quantity of labeled training samples and a large pool of additional unlabeled documents. The effectiveness of the framework is demonstrated and confirmed by some experiments of the framework on two popular text classification corpora.
Comprehensive Python module for computing and visualizing dynamic time warping alignment: DTWPy

Dynamic Time Warping (DTW) is a well-known technique used to determine alignment between two temporal sequences. DTW has been used in wide range of applications and it can be applied on any data which can be represented as linear sequence. Existing DTW libraries have out dated implementation of core DTW algorithm, which result in low performance or are inapplicable for big sequences. The aim of this thesis is to present, a comprehensive DTW library, encapsulating t implementation of DTW variants with recently proposed efficient algorithms. In this thesis I presented a python module for computing and visualizing DTW alignment: DTWPy. DTWPy has implementation of classical DTW, almost all the DTW variants proposed in literature to date, with recently proposed performance efficient algorithms i.e. fastDTW and DDTW. Algorithms implemented in DTWPy are tested thoroughly and give correct results. Correctness is verified by comparing it with existing R implementation. Furthermore, I have compared algorithms implemented, and performance of DTWPy with existing libraries. DTWPy have the most comprehensive implementation of DTW algorithms present in literature to date, and is applicable on large temporal sequence. The architecture of DTWPy is designed to be flexible, to scale and accommodate possible future improvements.
Multi-party conversation summarization using time information and text segmentation

In this paper, we propose a method to summarize a multi-party conversation. The method consists of two processes; important utterance extraction and summary generation using the extracted utterances. For the extraction process, we apply time features as new nonverbal information. For the generation process, we utilize a text segmentation method, LCSeg, to improve the coverage of the summary. The experimental result shows the effectiveness of the time features in the extraction process.
Image Recomposition Using Seam Carving and Insertion Considering the Rule of Thirds

In this paper, we present an algorithm for adjusting the position of a user-specified object considering image aesthetics. Specifically, the user-specified object is positioned according to the rule of thirds by inserting or deleting unimportant seam lines from the image. To find such seam lines, a novel weight map is designed using the spatial and color distances from the object. We also design and analyze two approaches to seam carving and insertion. Experimental results show that the proposed method can be used as an effective semi-automatic image recomposition scheme.
Direct Benzylic Oxidation withSodium Hypochlorite Using a New Efficient Catalytic System: TEMPO/Co(OAc)2

Direct benzylic oxidation of arenes was achieved using NaClO/TEMPO/Co(OAc) 2 . Various aromatic aldehydes and ketones were obtained from alkylarenesdirectly by benzylic oxidation in good to excellent yields. Thereaction reactivity, selectivity, and scope of the reaction wereinvestigated.
Bayesian Importance Measure-Based Approach for Optimal Redundancy Assignment

SYNOPTIC ABSTRACT The present work resolves a reliability optimization problem, using assignment of redundancy via Bayesian importance. If a system fails, it is of interest to know which component has caused the failure of the system and how important the different components are for the functioning or failing of the system. Bayesian importance of a component is a measure that reflects its role in system failure, and hence, it might be a good idea to strengthen a system component with a high value of Bayesian importance. Thus, this measure can serve as a basis for making a decision with regard to selecting the right system component for maximizing system reliability through adding redundancy to the said component. Some general results have been proved toward optimal assignment of redundancy in complex coherent systems. The method developed here is capable of accommodating any structure function and any component-life distribution. The procedure has been illustrated through numerical examples in the context of some complex coherent systems.
Deformation mechanism of Area C landslide in Suijiang, Xiangjiaba Reservoir, Yunnan Province, China

The Xiangjiaba project is located in the Shuifu County, Yunnan Province. It is situated approximately 157 km away from the Xiluodu project, 33 km away from the Yibin City and 1.5 km away from the Shuifu County. The Area C landslide of Suijiang County, with a volume of 47 million m3, is located on the southern bank of the Xiangjiaba project. The landslide body has been experiencing persistent deformation with a recorded maximum horizontal displacement of 1,480 mm up to December 2015, posing a critical threat to the lives and properties of local residents. Based on the data collected from detailed geological surveys and three-year monitoring, this paper presents the findings of an in-depth study about deformation characteristics and influence factors of Area C landslide. The findings indicate that: the high southern terrain, steep back rock, wavy-bedded sandstone and mudstone interbeds and hydrophilic soil provide necessary conditions for the landslide. The Area C is located in ancient landslide sites, a secondary shear exists in its front. During the rainy season, the displacement rate of Area C increased sharply, and the deformation is more closely associated with the sudden drop of reservoir water than with the sudden rise. The safety factor decreases under reservoir inundation and rainfall. Continuous creep contributes to the movements of Area C landslide, though the movements are slow. The possibility of high-speed slip is very small. Relevant measures should be taken to protect the safety of lives and properties of the immigrants.
Natural language processing techniques for the purpose of sentinel event information extraction

An approach to biomedical language processing is to apply existing natural language processing (NLP) solutions to biomedical texts. Often, existing NLP solutions are less successful in the biomedical domain relative to their non-biomedical domain performance (e.g., relative to newspaper text). Biomedical NLP is likely best served by methods, information and tools that account for its particular challenges. In this thesis, I describe an NLP system specifically engineered for sentinel event extraction from clinical documents. The NLP system's design accounts for several biomedical NLP challenges. The specific contributions are as follows. (1) Biomedical tokenizers differ, lack consensus over output tokens and are difficult to extend. I developed an extensible tokenizer, providing a tokenizer design pattern and implementation guidelines. It evaluated as equivalent to a leading biomedical tokenizer (MedPost). (2) Biomedical part-of-speech (POS) taggers are often trained on non-biomedical corpora and applied to biomedical corpora. This results in a decrease in tagging accuracy. I built a token centric POS tagger, TcT, that is more accurate than three existing POS taggers (mxpost, TnT and Brill) when trained on a non-biomedical corpus and evaluated on biomedical corpora. TcT achieves this increase in tagging accuracy by ignoring previously assigned POS tags and restricting the tagger's scope to the current token, previous token and following token. (3) Two parsers, MST and Malt, have been evaluated using perfect POS tag input. Given that perfect input is unlikely in biomedical NLP tasks, I evaluated these two parsers on imperfect POS tag input and compared their results. MST was most affected by imperfectly POS tagged biomedical text. I attributed MST's drop in performance to verbs and adjectives where MST had more potential for performance loss than Malt. I attributed Malt's resilience to POS tagging errors to its use of a rich feature set and a local scope in decision making. (4) Previous automated clinical coding (ACC) research focuses on mapping narrative phrases to terminological descriptions (e.g., concept descriptions). These methods make little or no use of the additional semantic information available through topology. I developed a token-based ACC approach that encodes tokens and manipulates token-level encodings by mapping linguistic structures to topological operations in SNOMED CT. My ACC method recalled most concepts given their descriptions and performed significantly better than MetaMap. I extended my contributions for the purpose of sentinel event extraction from clinical letters. The extensions account for negation in text, use medication brand names during ACC and model (coarse) temporal information. My software system's performance is similar to state-of-the-art results. Given all of the above, my thesis is a blueprint for building a biomedical NLP system. Furthermore, my contributions likely apply to NLP systems in general.
Numerical algorithm of distributed TOPKAPI model and its application

For the sake of improving the calculation precision of the distributed TOPKAPI model,a numerical algorithm was applied to solve the non-linear reservoir differential equations in the model.The general form of these equations was derived.The recurrence formula was constituted using the algorithm of the fourth-order Runge-Kutta method.The method of variable steps improved the efficiency of the algorithm and guaranteed the convergence and stability of the formula.The numerical algorithm of the fourth-order Runge-Kutta with variable steps was applied in the flood simulation of the Buliu River watershed with a grid precision of one kilometer.The deterministic coefficients of model calibration and validation reached 0.908 and 0.912,respectively.
The autonomous frontal obstacle avoidance system with trajectory updating function

Popularization of active safety technologies for automobile such as ABS and ESC has contributed to a remarkable decrease of the number of traffic accidents; however, over 4,000 traffic fatalities a year still occur in Japan. To realize society of zero traffic accidents, further technological innovations are needed. Since approximately 40% of drivers do not take any avoidance maneuver at the time of a traffic accident (ITARDA, 2005), automatic emergency braking systems (AEB) are now expected to reduce the number of accidents. In addition, since it has been reported that collision avoidance by steering is more effective at high speed (Horiuchi et al., 2006), collision avoidance technologies by evasive steering will also be effective to reducing the number of accidents. Collision avoidance technologies by steering are still in the research phase. Many researches are being promoted from various viewpoint. For example, Glacer et al. (2010) have proposed a maneuver-based trajectory planning in which the avoidance trajectory is expressed as a polynomial. Soudbakhsh et al. (2014) also utilize a polynomial trajectory and proposed a trajectory optimization method to meet the limitation of lateral acceleration. Other researches include evasive motion control methods of the vehicle in which avoidance trajectories are not derived explicitly. Fujioka et al. (2008) proposed the optimal vehicle motion control method which minimizes the collision risk value and Ohmuro et al. (2010) proposed that which minimizes the resultant vehicle force. Shibata et al. (2014) utilize potential field method, which is commonly used in robotics. Fuzzy control has been also utilized in collision avoidance by steering (Llorca et al., 2011). The authors have also proposed a collision avoidance technology using steering. The main feature is that it utilizes
Privacy-preserving for Location-based Service over Encrypted Data Search

In location-based service system,it is a vital problem to protect user's privacy including identity privacy,location privacy and preference privacy,and gain high quality service for users at the same time.This paper proposed a privacy-preserving scheme based on encrypted data search.Location-based service provider outsources its encrypted data and index to cloud server who executes user's LBS queries and returns the top-K results to user according to matching score.The scheme does not rely on user's cooperation and any trusted third party.Finally theoretical and experimental analysis shows that the proposed scheme can effectively protect user's identity,location and preference privacy with a lower computation and communication overhead.
Program Analyses for Understanding the Behavior and Performance of Traditional and Mobile Object-Oriented Software

The computing industry has experienced fast and sustained growth in the complexity of software functionality, structure, and behavior. Increased complexity has led to new challenges in program analyses to understand software behavior, and in particular to uncover performance inefficiencies. Performance inefficiencies can have significant impact on software quality. When an application spends a substantial amount of time performing redundant work, software performance and user experience can deteriorate. Some inefficiencies can use up certain types of resources and lead to program crashes. In general, performance inefficiency is an important and challenging problem for modern software systems. It is also a shared problem for traditional and mobile object-oriented software. Static and dynamic analyses need to keep up with this trend, and this often requires novel technical approaches. One important symptom of performance inefficiencies is run-time bloat : excessive memory usage and work to accomplish simple tasks. Bloat significantly affects scalability and performance, and exposing it requires good diagnostic tools. As the first contribution of this dissertation, we present a novel analysis that profiles the run-time execution to help programmers uncover potential performance problems. The key idea of the proposed approach is to track object references, starting from object creation statements, through assignment statements, and eventually ending at statements that perform useful operations. An abstract view of reference propagation
Using Data Flow Analysis to Determine Bi-Directional Ripple Effects During Software Maintenance

Ripple analysis can be used to aid in the understanding of unfamiliar code and in debugging. This paper describes a static code analysis program that allows software maintainers to monitor potential bi-directional ripple side-effects caused by modifications to Pascal source and presents data from analyses of actual code. This program utilizes data flow analysis techniques to collect flow insensitive information about the variables in each source statement and build a database containing a call, control flow, and dead graph. An interesting observation is that forward ripples require less computation on the average than backward ripples. A ripple algorithm that performs a graph traversal to identify reverse and forward side-effects for a given variable on a given source line is described.