Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized and Efficient Authentication in Mobile Environments

In a mobile environment, a number of users act as a network nodes and communicate with one another to acquire location based information and services. This emerging paradigm has opened up new business opportunities and enables numerous applications such as road safety enhancement, service recommendations and mobile entertainment. A fundamental issue that impacts the success of these applications is the security and privacy concerns raised regarding the mobile users. In that, a malicious user or service provider can track the locations of a user traveled so that other malicious act can be carried out more effectively against the user. Therefore, the challenge becomes how to authenticate mobile users while preserving their actual identity and location privacy. In this work, we propose a novel randomized or privacy-preserving authentication protocol based on homomorphic encryption. The protocol allows individual users to self generate any number of authenticated identities to achieve full anonymity in mobile environment. The proposed protocol prevents users being tracked by any single party including peer users, service providers, authentication servers, and other infrastructure. Meanwhile, our protocol also provides traceability in case of any dispute. We have conducted experimental study which demonstrates the efficiency of our protocol. Another advantage of the proposed protocol is lightweight computation and storage requirement, particularly suitable for any mobile devices with limited computation power and storage space.
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning

Positive instance detection, especially for these in positive bags (true positive instances, TPIs), plays a key role for multiple instance learning (MIL) arising from a specific classification problem only provided with bag (a set of instances) label information. However, most previous MIL methods on this issue ignore the global similarity among positive instances and that negative instances are non-i.i. d. , usually resulting in the detection of TPI not precise and sensitive to outliers. To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately. PIGMIL selects instances from working sets (WSs) of some working bags (WBs) as positive candidate pool (PCP). The global similarity among positive instances and the robust discrimination of instances of PCP from negative instances are measured to construct the consistent similarity and discrimination graph (CSDG). As a result, the primary goal (i.e. TPI detection) is transformed into PCP updating, which is approximated efficiently by updating CSDG with a random walk ranking algorithm and an instance updating strategy. At last bags are transformed into feature representation vector based on the identified TPIs to train a classifier. Extensive experiments demonstrate the high precision of PIGMIL's detection of TPIs and its excellent performance compared to classic baseline MIL methods.
Rearrest rates after incarceration for DWI: a comparative study in a southwestern US county.

OBJECTIVES This study was undertaken to assess a 28-day detention and treatment program's effect, in a multiethnic county with high rates of alcohol-related arrests and crashes, on first-time offenders sentenced for driving while impaired (DWI). METHODS We used comparison of baseline characteristics, survival curves of subsequent arrest, and Cox proportional hazards regression to examine probability of rearrest of those sentenced and those not sentenced to the program. RESULTS Probability of not being rearrested was significantly higher for the treatment group after adjustment for covariates. At 5 years, probability of not being rearrested for the treatment vs the nontreatment group was 76.6% vs 59.9%. CONCLUSIONS Results suggest that this county's program has significantly affected rearrest rates for Native Americans, Hispanics, and non-Hispanic Whites.
Voice Production Mechanisms following Phonosurgical Treatment of Early Glottic Cancer

Objectives: Although near-normal conversational voices can be achieved with the phonosurgical management of early glottic cancer, there are still acoustic and aerodynamic deficits in vocal function that must be better understood to help further optimize phonosurgical interventions. Stroboscopic assessment is inadequate for this purpose. Methods: A newly developed color high-speed videoendoscopy (HSV) system that included time-synchronized recordings of the acoustic signal was used to perform a detailed examination of voice production mechanisms in 14 subjects. Digital image processing techniques were used to quantify glottal phonatory function and to delineate relationships between vocal fold vibratory properties and acoustic perturbation measures. Results: The results for multiple measurements of vibratory asymmetry showed that 31% to 62% of subjects displayed higher-than-normal average values, whereas the mean values for glottal closure duration (open quotient) and periodicity of vibration fell within normal limits. The average HSV-based measures did not correlate significantly with the acoustic perturbation measures, but moderate correlations were exhibited between the acoustic measures and the SDs of the HSV-based parameters. Conclusions: The use of simultaneous, time-synchronized HSV and acoustic recordings can provide new insights into postoperative voice production mechanisms that cannot be obtained with stroboscopic assessment.
Piezoelectric Effect on Spin Transport Characteristics of Ferromagnet/Semiconductor Junction

The spin transport characteristics of junction consisting of ferromagnet/semiconducting curved nanowire are investigated. The semiconducting nanowire, in the present paper, is ZnO. For a curved ZnO nanowire, tensile strain and compressive strain occur at the outer and inner surfaces, respectively. So, ZnO produces piezotronic effects when it is strained. The aim of the present paper is to study the effect of strain on the spin transport characteristics, e.g., the conductance, spin polarization and giant magnetoresistance. This can be achieved by using the effective mass approximation method and Floquet theory. Spin-orbit coupling, the effect of ac-field and magnetic field are taken into consideration. Results show that the spin transport characteristics are highly sensitive to strain mainly due to the change in Schottky barrier height. This change in Schottky barrier height is owing to the strain induced band structure change and piezoelectric effect. The present results show that this investigation is very important and the junction studied could be fabricated for tips of scanning tunneling microscopy for sensing strain in nanostructured materials.
Intuitive and Natural Interfaces for Geospatial Data Classification

1 Cognitive Systems, University of Bremen, Germany, schmid@informatik.uni-bremen.de 2 Research Center Spatial Cognition (SFB/TR 8), University of Bremen, Germany, okutz@informatik.uni-bremen.de 3 Capacity Lab, University of Bremen, Germany, lutz@capacitylab.org 4 DFKI Lab Bremen, Germany, Till.Mossakowski@dfki.de 5 MUSIL, University of Muenster, Germany, tomi.kauppinen@uni-muenster.de 6 GeoCrowd, University of Bremen, Germany, cai@informatik.uni-bremen.de
2007 Annual Computer Security Application Conference Work in Progress Abstract Proposal Towards securing inter-device communication: Applying Inter-device Authentication and Authorization Framework to Home Appliances

Future networks everywhere will be connected to innumerable Internet-ready home appliances. A device accepting connections over a network must be able to verify the identity of a connecting device in order to prevent device spoofing and other malicious actions. In recent works, some major existing home network specifications consider device-specific security functions. For example, UPnP Security [1] has a sophisticated mechanism to guarantee personal ownership of a device. UPnP Security defines a special terminal device called “Security Console” to set up security configurations on each device remotely. UPnP Security also has an inter-device authentication and authorization mechanism using a public key pair, Security ID and ACL. Bluetooth [2] has a secure simple pairing mechanism to take ownership of a device. After the taking ownership of the device, two devices generate a shared secret (called a link key) for future mutual authentication. As mentioned above, some major specifications already consider a security mechanism for devices. We have also proposed a novel security mechanism not for human being but for devices [3]. In this report, we show a practical tiny security surveillance system for conventional home appliances to achieve our proposed inter-device authentication and authorization framework.
An integrated decision support system for global manufacturing co-ordination in the automotive industry

Global manufacturing increasingly faces decision challenges of how to better manage the dependencies between different activities that take place either locally or across different locations. Co-ordination decision making not only requires the right information to be provided in the right place at the right time, but also requires the right level of support from models for decision analysis and decision evaluation. Furthermore, the alignment of co-ordination decisions with a global firm's global environment and its operations performance has been identified as crucial to the firm's success, but remains a challenge to decision makers. This paper proposes an integrated decision support system (IDSS) that can facilitate manufacturing managers to make more efficient and effective global co-ordination decisions. A combination of qualitative and quantitative analysis and assessment functions has been provided through the system's four key components (a global context modeller, a multi-criteria scoring modeller, a configurator and a co-ordinator). The evaluation of the decision system has been undertaken through a case study within the automotive industry, which demonstrates the applicability of the system to provide decision support for realistic global manufacturing co-ordination problems.
Abrogation of T cell quiescence characterizes patients at high risk for multiple sclerosis after the initial neurological event

Clinically isolated syndrome (CIS) refers to the earliest clinical manifestation of multiple sclerosis (MS). Currently there are no prognostic biological markers that accurately predict conversion of CIS to clinically definite MS (CDMS). Furthermore, the earliest molecular events in MS are still unknown. We used microarrays to study gene expression in naïve CD4+ T cells from 37 CIS patients at time of diagnosis and after 1 year. Supervised machine-learning methods were used to build predictive models of disease conversion. We identified 975 genes whose expression segregated CIS patients into four distinct subgroups. A subset of 108 genes further discriminated patients in one of these (group 1) from other CIS patients. Remarkably, 92% of patients in group 1 converted to CDMS within 9 months. Consistent down-regulation of TOB1, a critical regulator of cell proliferation, was characteristic of group 1 patients. Decreased TOB1 expression at the RNA and protein levels also was confirmed in experimental autoimmune encephalomyelitis. Finally, a genetic association was observed between TOB1 variation and MS progression in an independent cohort. These results indicate that CIS patients at high risk of conversion have impaired regulation of T cell quiescence, possibly resulting in earlier activation of pathogenic CD4+ cells.
Interfacing parallel applications and parallel databases

The use of parallel database systems to deliver high performance has become quite common. Although queries submitted to these database systems are executed in parallel, the interaction between applications and current parallel database systems is serial. As the complexity of the applications and the amount of data they access increases, the need to parallelize applications also increases. In this parallel application environment, a serial interface to the database could become the bottleneck in the performance of the application. Hence, parallel database systems should support interfaces that allow the applications to interact with the database system in parallel. We present a taxonomy of such parallel interfaces, namely the Single Coordinator, Multiple Coordinator, Hybrid Parallel, and Pure Parallel interfaces. Furthermore, we discuss how each of these interfaces can be realized and in the process introduce new constructs that enable the implementation of the interfaces. We also qualitatively evaluate each of the interfaces with respect to their restrictiveness and performance impact.
ErbB3 expression and dimerization with EGFR influence pancreatic cancer cell sensitivity to erlotinib

Abnormal expression and signaling of ErbB receptors has been implicated in multiple epithelial malignancies, including pancreatic cancer. Erlotinib, an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), has been recently approved for pancreatic cancer treatment, but there are no reliable predictors of patient response. Expression of additional ErbB receptors seems to influence tumor response to EGFR-targeted therapy. We analyzed the influence of ErbB3 expression on pancreatic cancer cell response to erlotinib treatment. Proliferation assays of five human pancreatic cancer cell lines were performed following treatment with erlotinib. Expression and phosphorylation profiles of ErbB receptors and downstream adaptor protein (Akt, ERK1/2, STAT3, mTOR) were evaluated following stimulation with EGF or neuregulin-β. The formation of EGFR homodimers and EGFR-ErbB3 heterodimers, necessary to enable ErbB3 downstream signaling, was demonstrated by chemical cross-linking assays. The effects of RNA inhibition of ErbB3 on sensitivity to erlotinib treatment were evaluated in AsPC-1 pancreatic cancer cells. Erlotinib inhibited Akt phosphorylation and proliferation of all the ErbB3-expressing cell lines but did not affect mTOR activation. Cross-linking studies confirmed the presence of EGFR-ErbB3 heterodimers in pancreatic cancer cells. Only the ErbB3-deficient MIA PaCa-2 cells displayed persistent Akt activation and ongoing proliferation in spite of erlotinib treatment. siRNA–mediated inhibition of ErbB3 expression in AsPC-1 cells resulted in acquired resistance to erlotinib treatment. Pancreatic cancer cells which lack ErbB3 do not display activation of the ErbB3-PI3K-Akt cascade induced by EGFR/ErbB3 heterodimers and become less critically dependent on EGFR signaling and therefore resistant to erlotinib. Pancreatic cancer expression of ErbB3 may be useful for EGFR-targeted therapy patient selection.
Dual Application Model for Agile Software Engineering

There are problems with Traditional Software Engineering and with Agile Software Development. A new approach called Agile Software Engineering that combines the best of both is proposed and an aspect of this approach described. The Dual Application Model involves the development of a logical software application focused on capturing the functional requirements and a physical software application focused on transforming the logical application to meet the non-functional requirements. It has advantages and disadvantages like any approach to software development but meets two of the principles proposed for Agile Software Engineering. Frameworks and tools are proposed to support the Dual Application Model but are not essential to the approach. The approach provides an almost complete separation of concerns between defining and specifying (in code) the domain solution / software problem for which the domain experts are primarily responsible and designing and implementing the software solution to meet the non-functional requirements for which the software developers are primarily responsible.
Center of attention: A network text analysis of American Sniper

Network Text Analysis (NTA) involves the creation of networks of words and/or concepts from linguistic data. Its key insight is that the position of words and concepts in a text network provides vital clues to the central and underlying themes of the text as a whole. Recent research has relied on inductive approaches to identify these themes. In this study we demonstrate a deductive approach that we apply to the screenplay of American Sniper, an Academy Award nominee for Best Adapted Screenplay in 2014. Specifically, we first use genre expectations theory to establish prior expectations as to the key themes associated with war films. We then empirically test whether words and concepts associated with the most influentially-positioned nodes are consistent with themes common to the war-film genre. As predicted, we find that words and concepts associated with the least constrained nodes in the text network were significantly more likely to be associated with the war, action, and biography genres and significantly less likely to be associated with the mystery, science-fiction, fantasy, and film-noir genres.
Preparation of functional DNA microarrays through laser-induced forward transfer

A functional DNA microarray was prepared through the laser-induced forward transfer (LIFT) technique. In a first experiment, droplets of a buffer solution were spotted onto a substrate at different laser pulse energies. This allowed one to determine that uniform spots with a diameter as small as 40μm could be obtained. In a second experiment, a microarray containing two different human cDNAs and a negative control was spotted through LIFT and submitted to a hybridization assay. The obtained results demonstrated the full functionality of the microarray, which allowed us to prove the viability of LIFT for the production of DNA microarrays.
MBD2 Is Required for Correct Spatial Gene Expression in the Gut

ABSTRACT Gene expression in the gut is segmentally regulated, but little is known of the molecular origin of patterning. Analysis of gene expression in colons from mice lacking the methyl-CpG binding repressor MBD2 revealed frequent activation of genes that are normally only expressed in the exocrine pancreas and duodenum. Reduced DNA methylation activated the same gene set in the colon. No significant differences in DNA methylation between the colon and duodenum were detected, but MBD2 was significantly more abundant in the colon. The relevance of MBD2 concentration was tested in a human colon cancer cell line. Depletion of MBD2 was again found to activate exocrine pancreatic genes. Gene activation in this cell culture model was accompanied by loss of promoter-bound MBD2 and increased histone acetylation. The results suggest that modulation of MBD2 during gut development establishes a region-specific gene expression pattern that is essential for establishing correct segmental character.
Intrinsically stable CCC for accurate measurement of small currents

We have developed and tested an intrinsically stable cryogenic current comparator (CCC) with a 1:20000 winding ratio for the accurate measurement of small electrical currents. Key feature of the CCC is the use of resistive CuNi wire and brass internal shields. First measurements of lOOpA to 300 pA currents using this CCC are performed at an uncertainty level of around 20 ppm to 50 ppm.
On the mutual information of glottal source estimation techniques for the automatic detection of speech pathologies

detection of speech pathologies by exploiting the estimation of the glottal source. Three methods of estimation are compared and time and spectral features are extracted. The relevancy of these features is assessed by means of information theory-based measures. This allows an intuitive interpretation in terms of discrimination power and redundancy between the features. It is discussed which features are informative or complementary for detecting voice pathologies and the glottal source estimation methods are compared.
Event Based Modeling for Context-Reactive Information Systems

Ubiquitous Information Systems (ISs) should support mobility of users and reason efficiently on their context to provide relevant and real-time information. ISs should be particularly adaptive and context-aware by managing and responding properly to occurring events. A need of integrating an adaptive and distributed event based system raises to lead to a diversity of proposed approaches and architectures. Unfortunately, the high level link between context and events is not explicitly considered in these systems. In this paper, we propose a metamodel coupling context and event metadata. The metamodel highlights some requirements considered in the definition of an event processing architecture to maximize communication security between distributed components. Context-aware event processing is a natural result of such architecture enabling the integration of context reasoning in event based systems. The proposal is used to implement an assistant prototype for passengers in the public transportation domain.
Compressed sensing with sparse, structured matrices

In the context of the compressed sensing problem, we propose a new ensemble of sparse random matrices which allow one (i) to acquire and compress a ρ0-sparse signal of length N in a time linear in N and (ii) to perfectly recover the original signal, compressed at a rate α, by using a message passing algorithm (Expectation Maximization Belief Propagation) that runs in a time linear in N. In the large N limit, the scheme proposed here closely approaches the theoretical bound ρ0 = α, and so it is both optimal and efficient (linear time complexity). More generally, we show that several ensembles of dense random matrices can be converted into ensembles of sparse random matrices, having the same thresholds, but much lower computational complexity.
Accelerated Distributed Average Consensus via Localized Node State Prediction

This paper proposes an approach to accelerate local, linear iterative network algorithms asymptotically achieving distributed average consensus. We focus on the class of algorithms in which each node initializes its ldquostate valuerdquo to the local measurement and then at each iteration of the algorithm, updates this state value by adding a weighted sum of its own and its neighbors' state values. Provided the weight matrix satisfies certain convergence conditions, the state values asymptotically converge to the average of the measurements, but the convergence is generally slow, impeding the practical application of these algorithms. In order to improve the rate of convergence, we propose a novel method where each node employs a linear predictor to predict future node values. The local update then becomes a convex (weighted) sum of the original consensus update and the prediction; convergence is faster because redundant states are bypassed. The method is linear and poses a small computational burden. For a concrete theoretical analysis, we prove the existence of a convergent solution in the general case and then focus on one-step prediction based on the current state, and derive the optimal mixing parameter in the convex sum for this case. Evaluation of the optimal mixing parameter requires knowledge of the eigenvalues of the weight matrix, so we present a bound on the optimal parameter. Calculation of this bound requires only local information. We provide simulation results that demonstrate the validity and effectiveness of the proposed scheme. The results indicate that the incorporation of a multistep predictor can lead to convergence rates that are much faster than those achieved by an optimum weight matrix in the standard consensus framework.