Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity of Coastal Currents near Point Conception to Forcing by Three Different Winds: ECMWF, COAMPS, and Blended SSM/I–ECMWF–Buoy Winds

Previous observational and modeling studies have indicated the importance of finescale winds in determining the circulation near Point Conception in the Santa Maria Basin (SMB) and the Santa Barbara Channel (SBC), California. There has not been a systematic attempt, however, to analyze and quantify the sensitivity of the near-surface circulation to different wind data. Here, a regional circulation model of the SMB and SBC is driven using three wind datasets: the European Centre for Medium-Range Weather Forecasts (ECMWF; 110 km 110 km horizontal grid), the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS; 9 km 9 km horizontal grid), and a blended wind product that combines Special Sensor Microwave Imager (SSM/I), ECMWF, and buoy and coastal wind data and that is referred to as SEB. A springtime period (March–May 1999) in which equatorward wind dominates and wind stress curls are strong is chosen for the study. Two groups of experiments are conducted: with and without assimilating moored temperature observations. The focus is on long time scales of greater than weeks and on mean currents. Comparisons between these experiments and between model and observation show that the circulation driven by the ECMWF wind is much weaker than that by the other two winds. On the other hand, the SEB dataset shows locally intensified wind stress curls behind capes and coastal bends, whereas these wind stress curls are weak in COAMPS. It is found that these small-scale variations in the wind field force alongshore inhomogeneous pressure gradients that in turn can significantly affect the near-coast currents. The result is that modeled currents forced by SEB agree better with observations than do those produced by COAMPS. Empirical orthogonal function analyses were conducted on the near-surface currents, sea level, wind, and wind stress curl. The mode-1 current (40%) is unidirectional (i.e., generally equatorward or poleward) and correlates well with the mode-1 wind (90%). The mode-2 current (20%) is cyclonic in the SBC and poleward inshore and equatorward offshore in the SMB; it correlates well with mode-1 sea level (70%), which suggests that mode-2 currents are driven by the pressure gradient. It is significant that neither mode-2 current nor mode-1 sea level correlates well with mode-1 wind stress curl (70%); rather, they correlate well with the time integral of the mode-1 wind stress curl. These conclusions support a previous theoretical idea that cyclonic circulation in the SBC and the inshore currents of the SMB are both driven by alongshore pressure setup induced by the time integral of the wind stress curl, rather than by the wind stress curl itself. This idea of a pressure setup is consistent with the differences found between the currents driven by COAMPS and SEB winds.
International Journal of Advanced Robotic Systems a Primal-dual Heuristic for a Heterogeneous Unmanned Vehicle Path Planning Problem Regular Paper

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract We consider a path planning problem where a team of Unmanned Vehicles (UVs) is required to visit a given set of targets. The UVs are assumed to carry different sensors, and as a result, there are vehicle-target constraints that require each UV to visit a distinct subset of targets. The objective of the path planning problem is to find a path for each UV such that each target is visited at least once by some vehicle, the vehicle-target constraints are satisfied and the total distance travelled by the vehicles is a minimum. This path planning problem is a generalization of the Hamiltonian path problem and is NP-Hard. We develop a primal-dual heuristic and incorporate the heuristic in a Lagrangian relaxation procedure to find good, feasible solutions and lower bounds for the path planning problem. Computational results show that solutions whose costs are on an average within 14% of the optimum can be obtained relatively quickly for the path planning problem involving five UVs and 40 targets.
A Kerberos-based UPnP Extension for Secure Home Networks

Communication and Information Security Lab Shenzhen Graduate School, Peking University, Shenzhen, China huiyaz@sz.pku. edu.cn, zhuys@pkusz.edu.cn Abstract. UPnP is a popular protocol in digital home network. However, no mature security mechanism is provided in current UPnP. In this paper, we propose a new UPnP extension based on Kerberos (KUPnP) to enhance the security of UPnP network. A Key Distribution Centre (KDC) is introduced to handle the mutual authentication between control points and devices. Also a security enhanced service “SecureKUPnPService” is provided for device template to perform secure handshake and build secure channel for network entities. Implementation results show that the control points and devices in UPnP home network can be authenticated with KUPnP and communications among all entities are secured.
Solving NP-hard number matrix games with Wisdom of Artificial Crowds

Solving puzzles based on number-sum or ordered matrices is an NP-hard problem that requires considerable computational effort. Prime examples of these are the games Sudoku and Kakuro. Kakuro relies on number sequences that must sum to a number indicator shown on the puzzle. Sudoku requires that numbers be listed in an implicit sequence in blocks and rows. Both puzzles require exclusivity of the numbers listed in each row and logical grouping. As a result, solving these puzzles requires an iterative approach. We show that Genetic Algorithms (GA) can be augmented with postprocessing by a Wisdom of Artificial Crowds (WoAC) to constrain the solution space after a number of generations. Using the WoAC method, compared to using GA alone, we can reduce the time to a successful solution by a factor of 50% for easy and medium-difficulty puzzles. Our work has broader applications in the fields of multi-agent theory and collective decision making, as the WoAC method allows for crowd-based improvements to well-known genetic algorithm methods.
Virtual Humanoids : Let ThemBeAutonomouswithoutLosing Control

There have been several efforts to build lifelike autonomous creatures in virtual worlds, but only few of them have focused their intentions in presenting human-like autonomous creatures. In this paper we discuss the problem of building autonomous virtual humanoids with goal directed behaviors. We present the decision-making as a process compound of: goal achievement planning, dynamic belief management, evolving goals, internal states and confidence levels. As simulating real humans, we applied to our model theories of Humans' Trust to be able to interact with the virtual humanoids and direct them at real time.
Sentiment Analysis

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, and emotions toward entities, events and their attributes. In the past few years, it attracted a great deal of attentions from both academia and industry due to many challenging research problems and a wide range of applications [1]. Opinions are important because whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations. However, there was almost no computational study on opinions before the Web because there was little opinionated text available. In the past, when an individual needed to make a decision, he/she typically asked for opinions from friends and families. When an organization wanted to find opinions of the general public about its products and services, it conducted surveys and focus groups. However, with the explosive growth of the social media content on the Web in the past few years, the world has been transformed. People can now post reviews of products at merchant sites and express their views on almost anything in discussion forums and blogs, and at social network sites. Now if one wants to buy a product, one is no longer limited to asking one’s friends and families because there are many user reviews on the Web. For a company, it may no longer need to conduct surveys or focus groups in order to gather consumer opinions about its products and those of its competitors because there is a plenty of such information publicly available.
The Press, the Audience, and Emotions in Italian Courtrooms (1860s–1910s)

Abstract:In late nineteenth-century Italy, the court, and especially the court of assizes, became a new and important public space: the law began permitting an audience in order to help control the judiciary power. This article shows that the court audience, hitherto placed at the margins of both historical narratives and historiographical analysis, played a key role in emotional interactions in the modern courtroom. Drawing primarily on court reports published in newspapers, this article offers a systematic and detailed analysis of the way in which the court audience's emotional expectations, dispositions, experiences, and practices were described and critically evaluated by the press, by the judicial and political authorities, and by legal and social scholars. Furthermore, it highlights the gender and class dynamics at play in the portrayal and perception of the audience at court hearings. Finally, it analyzes the complex relationship between the court audience itself and consumers of court news. This article demonstrates that research on law and emotions should focus not only on the primary actors in the courtroom, that is the judge, defendant, plaintiff, and witnesses, but also on those at the margins: the audience in the courtroom as well as the virtual audience of consumers of court news. In doing so, it shows that the emotional interaction in the courtroom was a two-way process: those on trial performed for their audience, but the audience's reactions also shaped the way in which future trials played out.
4 Integrative Approach for Detection of Functional Modules from Protein-Protein Interaction Networks

Advances in large scale technologies in proteomics, such as yeast two-hybrid (Y2H) screening and mass spectrometry (MS) have enabled us to generate large protein-protein interaction (PPI) networks. The structure of such networks has been frequently analysed to identify the modules, which constitute the basic “building blocks” of molecular networks. One of the challenges that systems biology is facing consists of explaining biological organisation in the light of the existence of modules in networks (Han et al., 2004; PereiraLeal et al., 2004; Petti and Church, 2005; Rives and Galitski, 2003). A series of studies attempting to reveal the modules in cellular networks, ranging from metabolic (Ravasz et al., 2002), to protein networks (Spirin and Mirny, 2003; Yook et al., 2004), support the proposal that modular architecture is one of the principles underlying biological organisation.
Spectral-based color separation using linear regression iteration

The present article will introduce a very simple new method for spectral-based color separation. This method inverts a Yule–Nielsen modified spectral Neugebauer model, utilizing its affine multilinearity in the 1/n-space. By means of linear regression, a sequence of colorant combinations is constructed converging to a colorant combination that approximates the desired reflectance spectrum in the sense of the smallest RMS error. Each iteration step consists mainly of two simple matrix–vector multiplications. With the aid of various simulation experiments, investigations on the speed of convergence are conducted. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 229–239, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20211
Automatic Generation of Valid Parallel-Processes Transition Using Genetic Algorithms and Communication-Flow Analysis

Parallel programming has become an essential programming paradigm in the last years. Testing of parallel programs is more difficult than that of sequential programs due to the anomalous effects which occur due to concurrency and interactions among the parallel processes. In parallel programs, states grow exponentially with the number of concurrent processes. Therefore, finding the valid sequence of transitions through a set of parallel processes is a demand and also a challenging task due to state explosion. In this paper, we present an approach based on genetic algorithms and communication-flow analysis for generating the valid sequence of transitions through the set of parallel processes. The approach uses the communication-flow analysis to generate all sending-receiving pairs between the set of parallel processes. Then, the proposed approach applies a genetic algorithm to generate a set of valid sequences of processes transition. This set of valid sequences of transitions is passed to a feasibility checker to find the proper executable one. We conducted a set of experiments to evaluate the proposed approach. The results of the experiments showed that our proposed approach is more efficient than the random technique that is used to generate the executable transition sequence through the parallel processes.
End-User Expectations During an Electronic Health Record Implementation: a Case Study in Two Academic Hospitals

Implementing an Electronic Health Record (EHR) is a sociotechnical process. End-users' expectations and attitudes need to be monitored during the implementation of a new EHR. It is not clear what end-users consider the most important determinants (both barriers and enablers for a successful EHR implementation) during this process. Based on the concept mapping method and a literature search we developed a questionnaire to investigate which determinants (future) end-users of an EHR consider important. Additionally we analysed whether there are differences between a centre working with a legacy EHR and one with paper-based patient records before implementation. We identified the following determinants: usability of EHR, availability of facilities, alignment with work processes, support during implementation, training on new EHR, support after implementation, practice with new EHR, internal communication, learning from other centres, reuse of patient data, general IT skills, and patient involvement in decision making. All determinants were perceived important by end-users to successfully work with an EHR directly after its go-live. The only two significant differences between centres were knowledge about the effect of the EHR on work processes, and importance of patient involvement in decision making.
Reduction of rejections in cold rolled strip welding by intelligent analysis of image and process data

Welding plays an important role in the metallurgic process, being a critical part of continuous processes. The early detection of welding defects is a key aspect to guarantee productivity. There are factories in which the welding testing is performed visually by an operator. In this scenario, the physiological and psychological aspects of the operator can determine the productivity due to unnecessary repetitions of welds. This paper proposes an on-line intelligent system for operator support. The goal is to reduce the unnecessary repetitions of welds. The proposed method uses data mining and machine learning techniques fed by the information extracted from the process data and from the data obtained by an infrared camera, creating an objective model that estimates the weld reliability. Flexibility and adaptability are two key concepts in the proposed design.
Learning features from vibration signals for induction motor fault diagnosis

Aiming at automated and intelligent state monitoring of induction motors, which are an integral component of a broad spectrum of manufacturing machines, this paper presents a Deep Belief Network (DBN)-based approach to automatically extract relevant features from vibration signals that characterize the working condition of an induction motor. The DBN model employs a structure with stacked restricted Boltzmann machines (RBMs), and is trained by an efficient learning algorithm called greedy layer-wise training. Vibration signals are used as the input to the DBN, and the outputs from activation functions of the trained network are the features needed for fault diagnosis. Comparing to traditional feature extraction methods for induction motor fault diagnosis such as wavelet packet transform, the proposed method is able to learn features directly from the vibration signal to achieve comparable performance with high classification accuracy. Experiments conducted on a machine fault simulator have verified the effectiveness of the proposed method for induction motor fault diagnosis.
Towards Cultural Competence: An Exploratory Study of the Relationship Between Racial Identity and Contextual Performance Indicators of Public Employees

HARRIS, ANN FAITH. Towards Cultural Competence: An Exploratory Study of the Relationship Between Racial Identity and Contextual Performance Indicators of Public Employees. (Under the direction of Dennis M. Daley.) This study examined the relationship between public employee racial identity status and contextual performance indicators defined as altruism, conscientiousness, organizational commitment, and self-efficacy. U.S. Census data indicates that Blacks tend to be over-represented in jobs with low to moderate levels of power and authority. This study contends that race membership may lack measurement sensitivity to pick up contextual performance group differences. Therefore, use of racial identity status is a better, culturally competent measure. Previous research suggests that organizational advancement and rewards are influenced by contextual performance and that supervisory performance ratings of subordinates are more reliant on contextual rather than objective factors. Since non-task performance is difficult to measure and reliant on single rater assessment, opportunities exist for bias to affect worker ratings. Some performance studies have associated achievement/performance gaps or perceived differences between Whites and Blacks with racial bias. Given the limited amount of research in this area, the relationship cannot be supported or refuted. To examine the role of racial self-identity, the analysis used a sample (N = 149) involving White (n = 84) and Black (n = 65) employees across six organizational units in a large southeastern public university. Racial self-identity was measured using Helms' (1990) White Racial Identity Attitude Scale (WRIAS) and Black Racial Identity Attitude Scale (BRIAS). Contextual performance indicators included altruism, conscientiousness, organizational commitment and self-efficacy. This field test was the first to use these concepts within the context of a public organization. Black RIAS Conformity/Immersion-Emersion, White RIAS Disintegration/Reintegration and Combined Racial Identity Measures CRIS 1/CRIS 2 scores were found to explain more variation in contextual performance than race membership alone. Specifically, Conformity, Reintegration and CRIS 1 were negative predictors of self-efficacy; Immersion-Emersion and Disintegration were positive predictors of self-efficacy; Contact was a positive predictor of conscientiousness; and Combined Racial Identity Scores 1 and 2 (the two lowest levels) were positive predictors of altruism. TOWARDS CULTURAL COMPETENCE: AN EXPLORATORY STUDY OF THE RELATIONSHIP BETWEEN RACIAL IDENTITY AND CONTEXTUAL PERFORMANCE INDICATORS OF PUBLIC EMPLOYEES
Dual Antenna for Physical layer UHF RFID collision cancelling

One of the main problems which lower the efficiency of passive UHF RFID system is the collision between the tags. Current solutions are based on collision avoidance using MAC Layer Protocols (e.g. FSA, BT algorithms), these can be time consuming process because they discard the signals of Physical Layer and then require signal retransmission. It's therefore an issue of great importance to develop anti-collision algorithms based on physical layer. In this preliminary work, we show how to separate multiple UHF RFID tag signals based on physical layer characteristics. DAZF (Dual Antenna Zero forcing) and DAMMSE (Dual antenna receiver using Minimum -Mean-Square-Error) are two approaches which exploit the spatial signatures in order to separate the collided signals into the components corresponding to the single tags. Performance results are compared using different simulation scenarios.
Inter-AS traffic patterns and their implications

This paper reports on a study of traffic patterns among autonomous systems (ASes), based on traces taken at various points in the Internet. The traces display a highly nonuniform distribution of traffic on flows between pairs of hosts, networks, and ASes. Aggregation along coarser granularities, such as networks or ASes, accentuates this nonuniform distribution. In one typical trace, for example, the top 9% of flows between ASes accounts for 86.7% of the packets and 90.7% of the bytes transmitted. A highly nonuniform traffic pattern suggests that routers need to maintain only limited QoS flow state. The paper discusses the implications of this phenomenon on different proposed QoS mechanisms.
First-year readjustment to family culture: The roles of generation status and parental attachment on re-entry shock

First-generation college students face many challenges and many successes as the first from their family to go to college. Although much research has been done to support this population of students in the transition and adjustment to the college culture, little research has been completed about the experiences they face upon their return home to their family culture. Due to the lack of shared experiences, first-generation college students may encounter an increase in psychological issues when re-assimilating to their family culture after engaging in the college culture. Because research has indicated that secure attachment bonds lessen the impact of psychological symptoms such as stress, anxiety, and isolation during the transition and adjustment period to college, it may be possible that strong attachment decreases these same psychological issues during the transition and adjustment back home. This study examined the differences of the re-entry experiences of first-year college students based on generation status to their family culture using a theoretical lens that investigated theories of re-entry shock and parental attachment. The presence of secure attachment bonds from family relationships and the impact of these bonds as they relate to transition and adjustment home were also explored. The research design considered the characteristics of gender and ethnicity and the role they played on the dependent variables of re-entry shock and parental attachment. Although the results of the study did not find statistically significant differences between groups regarding re-entry shock, significant differences were found in regard to parental attachment bonds based on gender and generation status. Understanding differences in parental bonds will assist student affairs practitioners in offering ongoing programs and services that not only assist students in their transition back to their family culture, but may also make parents and families more aware of the opportunities and challenges that their students face throughout the college journey.