Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship-based university-industry links and open innovation: towards a research agenda

Recent research suggests that organizations increasingly rely on external sources of innovation via interorganizational network relationships. This article explores the diffusion and characteristics of collaborative relationships between universities and industry, and develops a research agenda informed by an 'open innovation' perspective. A framework is proposed that distinguishes university-industry relationships from other mechanisms based on technology transfer or human mobility. On the basis of the existing body research, the role of practices such as collaborative research, university-industry research centres, contract research and academic consulting compared to other university-industry links is analyzed. The evidence suggests that multiplex forms of relationship-based links between universities and industry are widely practiced whereby differences exist across industries and scientific disciplines. While most existing research focuses on the effects of university-industry links on innovation-specific variables, such as patents or firm innovationess, the organizational dynamics of these relationships remain under-researched. A detailed research agenda addresses research needs in two main areas: search and match processes between universities and firms, and the organization and management of collaborative relationships.
Service quality and human resource management

Service organisations are striving to increase the quality of the services they offer. They are also using a wide variety of people management techniques. These two activities can sometimes come into conflict. This article examines a variety of management practices, particularly from human resource management (HRM), used by the service sector, and assesses their potential impact on service quality and total quality management (TQM). Many techniques are identified as being potentially supportive of quality improvement but some pose threats, particularly those most closely associated with cost minimisation and the less subtle forms of managerial control. In order to encourage research in this area a number of research propositions and an agenda for further research are developed.
Camera anomaly detection based on morphological analysis and deep learning

Recently, camera anomaly detection has attracted increasing interest in order to generate real-time alerts of camera malfunction for video surveillance systems. The existing camera anomaly detection methods still haven't enough ability to detect comprehensive types of anomaly, and lack the self-improvement ability in the case of miscarriage of justice by self-learning. So, this paper proposes a morphological analysis and deep learning based camera anomaly detection method to detect comprehensive types of anomaly. Morphological analysis is used to detect simple camera anomalies to accelerate the processing speed, and deep learning is utilized to detect complicated camera anomalies to improve the accuracy. The experimental results show that the detection accuracy of the proposed method achieves more than 95%.
Spectral CT Reconstruction With Image Sparsity and Spectral Mean

Photon-counting detectors can acquire x-ray intensity data in different energy bins. The signal-to-noise ratio of resultant raw data in each energy bin is generally low due to the narrow bin width and quantum noise. To address this problem, here we propose an image reconstruction approach for spectral CT to simultaneously reconstruct x-ray attenuation coefficients in all the energy bins. Because the measured spectral data are highly correlated among the x-ray energy bins, the intraimage sparsity and interimage similarity are important prior knowledge for image reconstruction. Inspired by this observation, the total variation and spectral mean measures are combined to improve the quality of reconstructed images. For this purpose, a linear mapping function is used to minimalize image differences between energy bins. The split Bregman technique is applied to perform image reconstruction. Our numerical and experimental results show that the proposed algorithms outperform competing iterative algorithms in this context.
The Reform of European Union Development Policy: Improving Strategies for Conflict Prevention, Democracy Promotion & Governance Conditionality. CEPS Working Document No. 182, March 2002

Improving external relations and the management of development aid is a key component of the current reform of the European Union (EU). In the course of the 1990s, the promotion of democracy, the strengthening of good governance and enhancing the rule of law progressively became both an objective and a condition for the EU development assistance with developing countries. Achieving these sometimes-contradictory goals in practice is a permanent challenge. This paper reviews the EU’s policies and strategies aimed at preventing conflict and responding to the crises of governance. It explores the difficult combination of democracy assistance and governance conditionality and its applicability to the prevention of democratic erosion in developing countries. While the EU mainly relies on a positive approach of support and inducement, it has also introduced, since 1995, provisions to suspend aid in the event of a sudden and persistent interruption of the democratisation process. The suspension mechanism enshrined in the cooperation agreement between the EU and ACP countries has made political dialogue the main strategic tool for achieving these varied purposes. Mainstreaming political dialogue into the cooperation forces the EC to focus more explicitly and more rigorously on issues of power, politics and democracy than it has done in the past. Governance conditionality constitutes a critical juncture between democracy promotion and conflict prevention strategies. The article is organised in three substantive sections. It successively addresses the policy, strategy and implementation dilemmas of EC democracy and governance activities in third countries, reviewing the policy responses of the EC to the crises of governance in Niger, Haiti, Cote d’Ivoire and Fiji. It argues that conducting structured political dialogue puts further demands on the management of aid. While punitive forms of political conditionality have proved largely ineffective, an incentive-based approach to governance conditionality could yield, if well managed, greater results. The article concludes with a series of proposals for enhancing the European Commission’s ability to manage political dialogue and governance conditionality to encourage democracy and prevent conflict.
Network Access and Security Issues in Ubiquitous Computing

Here are several topics that we would like to discuss and explore at the workshop. These are wireless and mobile infrastructure, security, applications and services, and mobile payments. Due to our current expertise and interest, we would like to focus on infrastructure and security issues and how problems of access, coverage, roaming, end-to-end security, reliability, location management and multicast communications could be addressed in the pervasive and ubiquitous environment.
A Moving Least Squares Based Approach for Contour Visualization of Multi-Dimensional Data

Analysis of high dimensional data is a common task. Often, small multiples are used to visualize 1 or 2 dimensions at a time, such as in a scatterplot matrix. Associating data points between different views can be difficult though, as the points are not fixed. Other times, dimensional reduction techniques are employed to summarize the whole dataset in one image, but individual dimensions are lost in this view. In this paper, we present a means of augmenting a dimensional reduction plot with isocontours to reintroduce the original dimensions. By applying this to each dimension in the original data, we create multiple views where the points are consistent, which facilitates their comparison. Our approach employs a combination of a novel, graph-based projection technique with a GPU accelerated implementation of moving least squares to interpolate space between the points. We also present evaluations of this approach both with a case study and with a user study.
Political Connection and Stock Returns: A Longitudinal Study

A stock market should display informational efficiency and, therefore, should appropriately reflect the value of political connections, if any value exists. Using a comprehensive data set that incorporates both obvious and less obvious political connections to firms in Thailand, we provide a longitudinal study which shows that higher realized stock returns are systematically associated with political connectedness. Consistent with the view that such a relationship provides economic rents, this finding is particularly prominent in more regulated industries. The politically connected premium is higher for higher level political connections and when the political bodies hold an equity stake in the firm.
An Informational Energy Approach to Feature Selection

In this work, we focus on machine learning methods for handling data sets containing large amounts of irrelevant information. We address two key issues: the problem of selecting relevant features, and the problem of weighting (ranking) these features. We describe our Energy Supervised Relevance Neural Gas (ESRNG) algorithm, a kernel method which uses the maximization of Onicescu’s informational energy as a criteria to compute the relevance of the input features for an LVQ classification system.
Creating View-dependent Texture Maps

We present a technique for blending multiple images of an object into a single, view-dependent texture map for that object. This technique can be used for image-based rendering, when the object is known, or for “painting” a view-dependent texture map of an object. The technique provides a structured mechanism for combining images at different resolutions, producing a mip-map like structure with the different levels constructed from different images. The user controls the camera angles for which a given image is valid. The problem of gaps caused by self-occlusion and non-overlapping images is also dealt with. This technique is also suitable for use on an object that will be animated.
Stacking Fault Energies of Tetrahedrally Coordinated Crystals

The energies of the intrinsic stacking fault in 20 tetrahedrally coordinated crystals, determined by electron microscopy from the widths of extended dislocations, range from a few mJ/m2 to 300 mJ/m2. The reduced stacking fault energy (RSFE: stacking fault energy per bond perpendicular to the fault plane) has been found to have correlations with the effective charge, the charge redistribution index of the crystals and with the c/ aratio of the wurtzite structure (for many of the diamond and zincblende crystals, the wurtzite structure has also been reported to exist as a metastable phase). The implication of the correlations has been discussed from the point of the relative stability of the crystals between the cubic structure and the hexagonal structure.
Designing a Message for Public Education Regarding Stroke: Does FAST Capture Enough Stroke?

Background and Purpose— Previous studies have shown poor public knowledge of stroke warning signs. The current public education message adopted by the American Heart Association lists 5 stroke warning signs (“suddens”). Another message called FAST (face, arm, speech, time) could be easier to remember, but it does not contain as many stroke symptoms. We sought to assess the percentage of stroke/transient ischemic attack (TIA) patients identified by both public awareness messages by examining presenting symptoms of all stroke/TIA patients from a large, biracial population in 1999. Methods— Cases of stroke who presented to an emergency department or were directly admitted were ascertained at all local hospitals by screening of ICD-9 codes 430 to 436, and prospective screening of emergency department admission logs, in 1999. Study nurses abstracted initial presenting symptoms from the medical record. All-cause 30-day case-fatality was calculated. Results— During 1999, 3498 stroke/TIA patients (17% black, 56% female) presented to an emergency department. Of these events, 11.1% had presenting symptoms not included in FAST, whereas 0.1% had presenting symptoms not included in the suddens. The FAST message performed much better for ischemic stroke and TIA than for hemorrhage, missing 8.9% of the ischemic strokes and 8.2% of the TIAs, versus 30.6% of intracerebral hemorrhage/subarachnoid hemorrhage cases. Case-fatality in patients missed by FAST was similar to patients with FAST symptoms (9.0% versus 11.6%, P=0.15). Conclusions— Within our population, we found that the FAST message identified 88.9% of stroke/TIA patients. The FAST message performed better for ischemic stroke and TIA than for hemorrhagic stroke. Whether the FAST message is easier to recall for the public than the “suddens” message has yet to be determined.
An inverse kinematics method for 3D figures with motion data

We present a new inverse kinematics method that utilizes the motion data for realtime control and editing. The key idea is to extract parameters necessary for inverse kinematics from the motion data. These parameters are the weight matrix, which determines the motion of the redundant joints, and the transformation functions that define the motion of the end effectors. The user can control the motion by dragging a body segment using a mouse, and the method calculates the new motion using the precomputed parameters. The method enables interactive editing, warping, and retargeting character motions.
Framework for a Hybrid Prognostics

Fault detection and isolation, or fault diagnostic, of physical systems has been subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in some situations, detecting and diagnosing faults may not be sufficient. In these cases, it is more interesting to anticipate the time of the fault, what is the purpose of prognostics. This latter activity aims at estimating the remaining useful life of systems by using three main approaches: data-driven prognostics, model-based prognostics and hybrid prognostics. Data-driven prognostics concerns the transformation of the raw monitoring data to relevant models or trends which are then used to continuously assess the health state of the system and predict its remaining useful life. This approach is easy to implement, but suffers from precision. In addition, the method is applied in most cases on single physical components (bearings, gears, belts, etc.) and thus the interaction between the components of the whole system is not addressed. Model-based prognostics (also called physics of failure prognostics) deals with analytical modelling of the system including its degradation. This approach gives more precise results, but it is difficult to apply on complex systems for which the construction of the behaviour and degradation models is not a trivial task. Finally, the hybrid approach combines both model-based and data-driven approaches by keeping their advantages. This paper presents a framework allowing the development of hybrid prognostics. The framework relies on two main phases: an offline phase and an online phase. The first phase concerns the construction of the nominal and degradation models, and the definition of the faults and performance thresholds needed to calculate the remaining useful life of the system. The second phase deals with the utilisation of the models and thresholds obtained in the first phase to detect the fault initiation, assess the current health state of the system, predict its future health state and calculate its remaining useful life.
Efficient mining of distance‐based subspace clusters

Traditional similarity measurements often become meaningless when dimensions of datasets increase. Subspace clustering has been proposed to find clusters embedded in subspaces of high‐dimensional datasets. Many existing algorithms use a grid‐based approach to partition the data space into nonoverlapping rectangle cells, and then identify connected dense cells as clusters. The rigid boundaries of the grid‐based approach may cause a real cluster to be divided into several small clusters. In this paper, we propose to use a sliding‐window approach to partition the dimensions to preserve significant clusters. We call this model nCluster model. The sliding‐window approach generates more bins than the grid‐based approach, thus it incurs higher mining cost. We develop a deterministic algorithm, called MaxnCluster, to mine nClusters efficiently. MaxnCluster uses several techniques to speed up the mining, and it produces only maximal nClusters to reduce result size. Non‐maximal nClusters are pruned without the need of storing the discovered nClusters in the memory, which is key to the efficiency of MaxnCluster. Our experiment results show that (i) the nCluster model can indeed preserve clusters that are shattered by the grid‐based approach on synthetic datasets; (ii) the nCluster model produces more significant clusters than the grid‐based approach on two real gene expression datasets and (iii) MaxnCluster is efficient in mining maximal nClusters. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 427‐444, 2009
An all-digital PLL for frequency multiplication by 4 to 1022 with seven-cycle lock time

An all-digital phase-locked loop (PLL) circuit in which resolution in the phase detector and digitally controlled oscillator (DCO) exactly matches the gate-delay time is presented. The pulse delay circuit is connected in a ring shape with 32 inverters (2/sup 5/ inverters). With the inverter gate-delay time as the time base, the pulse phase difference is detected simultaneously with the generation of the output clock. In this system, the phase detector and oscillator share a single ring-delay-line (RDL). This means the resolution is the same at all times, making a high-speed response possible. In a prototype integrated circuit (IC) using 0.65-/spl mu/m CMOS, the generation of a frequency multiplication clock was achieved with four reference clocks, and that of a phase-locked clock with seven reference clocks, for a high-speed response. The cell size was 1.08 /spl times/ 1.08 mm/sup 2/, and the output clock frequency had a wide range of 50 kHz/spl sim/60 MHz. The multiplication range of the clock frequency was also a very wide 4/spl sim/1022, and a high level of precision was achieved with a clock jitter standard deviation of 234 ps. This digital PLL can withstand a broad range of operating environments, from -30/spl deg/C/spl sim/140/spl deg/C, and is suitable for making a programmable clock generator on a chip.
Computing Conformal Invariants: Period Matrices

Abstract. This work introduces a system of algorithms to compute period matrices for general surfaces with arbitrary topologies. The algorithms are intrinsic to the geometry, independent of surface representation. The computation is efficient, stable and practical for real applications. The algorithms are experimented to real surfaces including human faces and sculptures, and applied for surface identification problem. It is the first work that is both theoretically solid, and practically robust and accurate to handle real surfaces.
Self-Learning Camera: Autonomous Adaptation of Object Detectors to Unlabeled Video Streams

Learning object detectors requires massive amounts of labeled training samples from the specific data source of interest. This is impractical when dealing with many different sources (e.g., in camera networks), or constantly changing ones such as mobile cameras (e.g., in robotics or driving assistant systems). In this paper, we address the problem of self-learning detectors in an autonomous manner, i.e. (i) detectors continuously updating themselves to efficiently adapt to streaming data sources (contrary to transductive algorithms), (ii) without any labeled data strongly related to the target data stream (contrary to self-paced learning), and (iii) without manual intervention to set and update hyper-parameters. To that end, we propose an unsupervised, on-line, and self-tuning learning algorithm to optimize a multi-task learning convex objective. Our method uses confident but laconic oracles (high-precision but low-recall off-the-shelf generic detectors), and exploits the structure of the problem to jointly learn on-line an ensemble of instance-level trackers, from which we derive an adapted category-level object detector. Our approach is validated on real-world publicly available video object datasets.
Mastering the art of persuasion intelligent tutoring system for presenters

Public speaking is a non-trivial task since it is affected by how nonverbal behaviors are expressed. Practicing to deliver the appropriate expressions is difficult while they are mostly given subconsciously. This paper presents our empirical study on the nonverbal behaviors of presenters. Such information was used as the ground truth to develop an intelligent tutoring system. The system can capture bodily characteristics of presenters via a depth camera, interpret this information in order to assess the quality of the presentation, and then give feedbacks to users. Feedbacks are delivered immediately through a virtual conference room, in which the reactions of the simulated avatars can be controlled based on the performance of presenters.
A Cross-Cultural Comparison of Trip Characteristics

Abstract The increasingly globalized economy has intensified the need to better understand consumers' travel behavior. The knowledge of culture's influence on trip characteristics is of great importance to tourism marketers specifically in selecting appropriate marketing strategies for each target market. The objective of this paper is to examine the relationship of culture to international visitors' trip characteristics by using secondary data from the Hong Kong Omnibus Tourism Survey. The investigation focuses on two groups of international air travelers to Hong Kong with distinctively different cultural backgrounds: Mainland Chinese and Americans. A comparison of demographic and trip characteristics of these two cultural groups is undertaken. The research findings reveal that there are statistically significant differences in the demographic and trip characteristics between these groups. Implications for Hong Kong's international tourism marketing strategies are drawn.
Multiwavelength Observations of a Flare from Markarian 501

We present multiwavelength observations of the BL Lacertae object Markarian 501 (Mrk 501) in 1997 between April 8 and April 19. Evidence of correlated variability is seen in very high energy (VHE; E ≳ 350 GeV) γ-ray observations taken with the Whipple Observatory γ-ray telescope, data from the Oriented Scintillation Spectrometer Experiment of the Compton Gamma Ray Observatory, and quick-look results from the All-Sky Monitor of the Rossi X-Ray Timing Explorer, while EGRET did not detect Mrk 501. Short-term optical correlations are not conclusive, but the U-band flux observed with the 1.2 m telescope of the Whipple Observatory was 10% higher than in March. The average energy output of Mrk 501 appears to peak in the 2-100 keV range, which suggests an extension of the synchrotron emission to at least 100 keV, the highest observed in a blazar and ~100 times higher than that seen in the other TeV-emitting BL Lac object, Mrk 421. The VHE γ-ray flux observed during this period is the highest ever detected from this object. The VHE γ-ray energy output is somewhat lower than the 2-100 keV range, but the variability amplitude is larger. The correlations seen here do not require relativistic beaming of the emission unless the VHE spectrum extends to ≳5 TeV.