Miracle-dz/UniFAR-FTUs huggingface dataset:
Expert Assessment on the Probability of Successful Remote Code Execution Attacks

This paper describes a study on how cyber security experts assess the importance of three variables related to the probability of successful remote code execution attacks – presence of: (i) non-executable memory, (ii) access and (iii) exploits for High or Medium vulnerabilities as defined by the Common Vulnerability Scoring System. The rest of the relevant variables were fixed by the environment of a cyber defense exercise where the respondents participated. The questionnaire was fully completed by fifteen experts. These experts perceived access as the most important variable and availability of exploits for High vulnerabilities as more important than Medium vulnerabilities. Non-executable memory was not seen as significant, however, presumably due to lack of address space layout randomization and canaries in the network architecture of the cyber defense exercise scenario.
An experimental validation of a low-cost indoor relative position localizing system for mobile robotic networks

In this work, an experimental validation of a low-cost indoor relative position localizing system for mobile robotic networks is described. In our framework, each robot is assumed to be equipped with a light emitter along with a camera, both pointing to the ceiling. In this way, each robot can see on the ceiling a constellation with its own position at the azimuth and the (relative) positions of its neighbors falling in its visual field. This allows to measure the relative distance and orientation among robots. The proposed localizing system can be thought as a convenient tool to validate, among the others, the efficacy of cooperative control algorithms. Several experiments are provided to show the effectiveness of the proposed localizing system for typical multi-robot tasks, such as rendezvous or formation control.
The therapeutic effects of SET/I2PP2A inhibitors on canine melanoma

Canine melanoma is one of the most important diseases in small animal medicine. Protein phosphatase 2A (PP2A), a well conserved serine/threonine phosphatase, plays a critical role as a tumor suppressor. SET/I2PP2A is an endogenous inhibitor for PP2A, which directly binds to PP2A and suppresses its phosphatase activity. Elevated SET protein levels have been reported to exacerbate human tumor progression. The role of SET in canine melanoma, however, has not been understood. Here, we investigated the potential therapeutic role for SET inhibitors in canine melanoma. The expression of SET protein was observed in 6 canine melanoma cell lines. We used CMeC-1 cells (primary origin) and CMeC-2 cells (metastatic origin) to generate cell lines stably expressing SET-targeting shRNAs. Knockdown of SET expression in CMeC-2, but not in CMeC-1, leads to decreased cell proliferation, invasion and colony formation. Phosphorylation level of p70 S6 kinase was decreased by SET knockdown in CMeC-2, suggesting the involvement of mTOR (mammalian target of rapamycin)/p70 S6 kinase signaling. The SET inhibitors, OP449 and FTY720, more effectively killed CMeC-2 than CMeC-1. We observed PP2A activation in CMeC-2 treated with OP449 and FTY720. These results demonstrated the potential therapeutic application of SET inhibitors for canine melanoma.
“Washrooms for Customers Only”: Space, Dignity, and Sh*itting in the City

“WASHROOMS FOR CUSTOMERS ONLY”: SPACE, DIGNITY, AND SH*TTING IN THE CITY Edith Wilson University of Guelph, 2019 Advisor(s): Dr. Mervyn Horgan This thesis examines the narratives that explain the existence of washrooms for customers only (W4CO) rules in the City of Toronto. Fifteen interviews were conducted with workers and managers at both chain and independent fast food and coffee shops along five strips in Toronto’s downtown core. In addition, a full listing of the public washrooms already available in the city was assembled, since no previous complete list existed. Descriptive statistics gathered show that 37.62% of the businesses in the research area have W4CO signs. Findings also show that although W4CO signs are much less frequent in independent shops, independent shops also have many fewer washroom uses per hour than chain shops. Findings from interviews show that the high prevalence of homelessness and the intersection of homelessness with mental health issues and drug addiction were concerns for workers. However, most interviewees were interested in polic y solutions and tried to separate their often difficult experiences with the washrooms in businesses from discourse that stigmatizes vulnerable populations.
Research Note - Codiffusion of Wireless Voice and Data Services: An Empirical Analysis of the Japanese Mobile Telecommunications Market

Wireless telecommunications have become over time a ubiquitous tool that not only sustains our increasing need for flexibility and efficiency, but also provides new ways to access and experience both utilitarian and hedonic information goods and services. This paper explores the parallel market evolution of the two main categories of wireless services---voice and data---in leading technology markets, inspecting the differences and complex interactions between the associated adoption processes. We propose a model that addresses specific individual characteristics of these two services and the stand-alone/add-on relationship between them. In particular, we acknowledge the distinction between the nonoverlapping classes of basic consumers, who only subscribe to voice plans, and sophisticated consumers, who adopt both services. We also account for the fact that, unlike voice services, data services rapidly evolved over time due to factors such as interface improvement, gradual technological advances in data transmission speed and security, and the increase in volume and diversity of the content and services ported to mobile Internet. Moreover, we consider the time gap between the market introduction of these services and allow for different corresponding consumer learning curves. We test our model on the Japanese wireless market. The empirical analysis reveals several interesting results. In addition to an expected one-way effect of voice on data adoption at the market potential level, we do find two-way codiffusion effects at the speed of adoption level. We also observe that basic consumers impact the adoption of wireless voice services in a stronger way compared to sophisticated consumers. This, in turn, leads to a decreasing average marginal network effect of voice subscribers on the adoption of wireless voice services. Furthermore, we find that the willingness of voice consumers to consider adopting data services is positively related to both time and penetration of 3G-capable handsets among voice subscribers.
Efficient Melodic Query Based Audio Search for Hindustani Vocal Compositions

Time-series pattern matching methods that incorporate time warping have recently been used with varying degrees of success on tasks of search and discovery of melodic phrases from audio for Indian classical vocal music. While these methods perform effectively due to the minimal assumptions they place on the nature of the sampled pitch temporal trajectories, their practical applicability to retrieval tasks on real-world databases is seriously limited by their prohibitively large computational complexity. While dimensionality reduction of the time-series to discrete symbol strings is a standard approach that can exploit computational gains from the data compression as well as the availability of efficient string matching algorithms, the compressed representation of the pitch time series itself is not well understood given the pervasiveness of pitch inflections in the melodic shape of the raga phrases. We propose methods that are informed by domain knowledge to design the representation and to optimize parameter settings for the subsequent string matching algorithm. The methods are evaluated in the context of an audio query based search for Hindustani vocal compositions in audio recordings via the mukhda (refrain of the song). We present results that demonstrate performance close to that achieved by time-series matching but at orders of magnitude reduction in complexity.
Malicious node detection using ID-based authentication technique

In ad-hoc as well as internet networks, there may exist several malicious nodes which may reduce system reliability and robustness of the network. Therefore, detection of malicious node in the networks is a vibrant research area. For this, several techniques have been adopted in the literature. In this paper, we proposed malicious node detection protocol using authentication technique for ad hoc as well as internet networks. As the protocol depends on one-way hash operation and does not consider any high computation such as exponentiation or pairing computation, so it achieves high performance. Besides, the number of message passes is also reduced to authenticate a node within the network. The proposed protocol is simulated using AVISPA software, and the simulation results make certain that the protocol is SAFE under OFMC and CL-AtSe models. In addition, the complexities of the protocol are satisfactory.
Portfolio risk evaluation: An approach based on dynamic conditional correlations models and wavelet multiresolution analysis

We analyzed the volatility dynamics of three developed markets (U.K., U.S. and Japan), during the period 2003-2011, by comparing the performance of several multivariate volatility models, namely Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC) and consistent DCC (cDCC) models. To evaluate the performance of models we used four statistical loss functions on the daily Value-at-Risk (VaR) estimates of a diversified portfolio in three stock indices: FTSE 100, S&P 500 and Nikkei 225. We based on one-day ahead conditional variance forecasts. To assess the performance of the abovementioned models and to measure risks over different time-scales, we proposed a wavelet-based approach which decomposes a given time series on different time horizons. Wavelet multiresolution analysis and multivariate conditional volatility models are combined for volatility forecasting to measure the comovement between stock market returns and to estimate daily VaR in the time-frequency space. Empirical results shows that the asymmetric cDCC model of Aielli (2008) is the most preferable according to statistical loss functions under raw data. The results also suggest that wavelet-based models increase predictive performance of financial forecasting in low scales according to number of violations and failure probabilities for VaR models.
Clinical Process Modeling and Performance Measurement in Hospitals

Quality of care is nowadays an important element of clinical health care delivery improvement efforts. Process-based quality measures can help, to support the effectiveness and efficiency of clinical care as well as to enhance a continuous performance measurement. Clinical process models can enable process monitoring and quality controlling in health care facilities. We present a clinical reference process model describing the integration of clinical meaningful Key Performance Indicators (KPIs) for acute diseases taking additionally into account the possibilities for automatic extraction of KPIs from clinical information systems and considering the characteristics of the clinical environment, such as flexible workflows and modular process structure during a patient's stay in hospital.
Analysis of minimum-variance and Wiener-filtered beamforming strategies

We show that the minimum-variance (MV),Wiener-filtered (WF), and other beamformers can be derived as approximations to the ideal-observer's strategy for lesion feature discrimination. We analyze breast lesion discrimination performance for five beamformers. Four of the five include matching filtering of receive-channel signal before summation, because there is no loss of task information in the RF signals due to beamforming. Differences among beamformers occur depending on how they prepare RF signals for demodulation. Generally the MV and WF beamformers decorrelate the echo signals the most before demodulation, which transfers more task information into the B-mode data. We find the WF beamformer performs better than others because it is the closest approximation to the ideal observer strategy. The MV beamformer requires an additional low-rank approximation that handicaps performance for discriminating four of the five lesion features, but performs well for the fifth task because reducing rank filters data that is well matched to the task 5 feature spectrum. However, similarly reducing the rank of the WF filter makes it the beamformer of choice.
Gender Earnings Differentials and Regional Economic Development in Urban China, 1988-97

Gender earnings differentials in urban China by region and their changes during the first decade of economic reform are examined. It is found that the female-male earnings ratio increased during the early stage of reform. The male earnings premium, overall, showed an increasing trend in the later stage of reform. Decomposition of the gender earnings differential reveals that a relatively lower percentage of the differential could be explained by gender differences in productive characteristics in the fast growing regions and in regions with a rapid pace of reform. The cross-sectional results highlight the possible existence of gender discrimination, particularly in the later stages of economic reform and development. Both market competition and the effects of wage decentralization play a role in shaping the gender earnings differentials. Gender earnings differentials varied by region and over time, generally in tandem with the pace of economic reform and development. The decomposition of the over time changes in the earnings gap indicated that improvement in the productive characteristics of females during the reform period constantly enhanced the earnings of females relative to those of males. The changes over time in the return to female characteristics, however, work to counter any narrowing of the gender earnings gap. Copyright © 2007 The Author; Journal compilation © International Association for Research in Income and Wealth 2007.
Use of Benzodiazepines in Alzheimer’s Disease: A Systematic Review of Literature

Background: Benzodiazepines are frequently prescribed in patients with Alzheimer’s disease. Unfortunately, studies evaluating their benefits and risks in these patients are limited. Methods: Clinical trials focusing on the effect of benzodiazepines on cognitive functions, disease progression, behavioral symptoms, sleep disturbances, and the general frequency of benzodiazepine use were included in this review. Published articles from January 1983 to January 2015 were identified using specific search terms in MEDLINE and PubMed Library according to the recommendations of The Strengthening the Reporting of Observational Studies in Epidemiology initiative. Results: Of the 657 articles found, 18 articles met predefined selection criteria and were included in this review (8 on frequency, 5 on cognitive functions, 5 on behavioral and sleep disturbances). The frequency of benzodiazepine use ranged from 8.5% to 20%. Five studies reported accelerated cognitive deterioration in association with benzodiazepine use. Two studies reported clinical efficacy for lorazepam and alprazolam to reduce agitation in Alzheimer’s disease patients. No evidence was found for an improvement of sleep quality using benzodiazepines. Conclusion: This systematic review shows a relatively high prevalence of benzodiazepine use but limited evidence for clinical efficacy in Alzheimer’s disease patients. However, there is a paucity of methodologically high quality controlled clinical trials. Our results underscore a need for randomized controlled trials in this area.
Global Trajectory Optimization on Multilane Roads

This paper names several risks with iterative optimization of vehicle trajectories modeled through the calculus of variations, as used recently in several approaches to automated driving, and demonstrates how these can be addressed by global optimization, while retaining the original, variational optimization goals. A method for transforming variational methods into Hidden Markov Models is applied to multilane scenarios, such as highways or larger city streets. A particular side-effect of this method is the capability of providing a fail-safe emergency trajectory at almost no further computational effort. The proposed global optimization is demonstrated on several real-world situations and compared to a ground truth of human maneuvers.
Distributed Algorithms for Maximum Clique in Wireless Networks

In communication networks such as social networks, wireless networks and biology networks, it is of importance to find all cliques which can help understand the network topology. The clique structures can also be utilized in facilitating message forwarding in wireless networks. For instance, using a set of cliques of maximal and disjoint, one of the nodes in each clique can be elected to forward messages, by which the duplicate message transmissions can be efficiently reduced. Further, it is well known that the maximum clique problem (MCP) is closely related to the maximum independent set and the vertex cover problems. In recent years, the fundamental problem of finding maximal cliques or maximum cliques has attracted lots of attentions. However, few of these works focus on distributed solutions in wireless networks. In this paper, we pay our attention to this missing corner of research. Specifically, we first give a distributed algorithm which can compute all maximal cliques in a wireless network represented by a graph. The algorithm takes O(n) time and uses O(mn) messages, where n is the number of nodes and m the number of edges. Then, with the proposed algorithms MCP (maximum clique problem) and UMCP (unique MCP), we show that a unique maximum clique can be selected from all maximal cliques in O(n) rounds and using O(mn) messages. To the best of our knowledge, our algorithms are the first deterministic distributed solutions for MCP in wireless networks.
Analysis of Evolving SoC Interconnect Protocols

To face evolving communication requirements in Systemon-Chip (SoC) designs, interconnect fabric capabilities can be improved by adopting new topologies or new protocols. This paper will focus on the latter approach, analyzing the scalability features under increasing system load of shared bus STBus and AMBA AXI interconnects as opposed to the widely deployed AMBA AHB architecture.
A Robotics Coordination Case Study

Robotics software require and provide a number of different functionalities, which are typically encapsulated in components that cooperate and compete in order to control the behavior of a robot. Cooperation and competition are forms of interaction among concurrent activities and so they have to be coordinated. In order to achieve a good level of reusability and flexibility the coordination and the computation (how the component provides the service) need to be managed separately. According to this principle we propose an innovative ap- proach based on two frameworks that are widespread in other domains such as the web services. We will show by means of a use case how we have implemented the component through the Service Component Architecture and how we have managed the coordination by means of the Abstract State Machine. In particular we will illustrate that is possible to change the coordination policy without modifying the implementation of the services provided by the component.
Predicting risk of 2-year incident dementia using the CAMCOG total and subscale scores.

BACKGROUND being able to identify individuals at high risk of dementia is important for diagnostics and intervention. Currently, there is no standard approach to assessing cognitive function in older aged individuals to best predict incident dementia. OBJECTIVE to identify cognitive changes associated with an increased risk of 2-year incident dementia using the Cambridge Cognitive Examination (CAMCOG). DESIGN longitudinal population representative sample aged 65+ years. METHODS individuals were from the Medical Research Council Cognitive Function and Ageing Study. Classification and Regression Tree analysis was used to detect the optimal cut-off value for the CAMCOG total, subscales and composite memory and non-memory scores, for predicting dementia. Sensitivity and specificity of each cut-off score were assessed. RESULTS from the 2,053 individuals without dementia at the first assessment, 137 developed dementia at the 2-year follow-up. The results indicate similar discriminative accuracy for incident dementia based on the CAMCOG total, memory subscale and composite scores. However, sensitivity and specificity of cut-off values were generally moderate. Scores on the non-memory subscales generally had high sensitivity but low specificity. Compared with the CAMCOG total score they had significantly lower discriminative accuracy. CONCLUSION in a population setting, cut-off scores from the CAMCOG memory subscales predicted dementia with reasonable accuracy. Scores on the non-memory scales have lower accuracy and are not recommend for predicting high-risk cases unless all non-memory subdomain scores are combined. The added value of cognition when assessed using the CAMCOG to other risk factors (e.g. health and genetics) should be tested within a risk prediction framework.
Field test results of an automated image retrieval system

The rapid identification of yield detracting mechanisms through integrated yield management is the primary goal of defect sourcing and yield learning. At future technology nodes, yield learning must proceed at an accelerated rate to maintain current defect sourcing cycle times despite the growth in circuit complexity and the amount of data acquired on a given wafer lot. As integrated circuit fabrication processes increase in complexity, it has been determined that data collection, retention, and retrieval rates will continue to increase at an alarming rate. Oak Ridge National Laboratory (ORNL) has been working with International SEMATECH (ISMT) to develop methods for managing the large volumes of image data that are being generated to monitor the status of the manufacturing process (Tobin et al, 1999 and 2000). This data contains an historical record that can be used to assist the yield engineer in the rapid resolution of manufacturing problems. To date, there are no efficient methods of sorting and analyzing the vast repositories of imagery collected by off-line review tools for failure analysis, particle monitoring, line width control, and overlay metrology. In this paper, we describe a new method for organizing, searching, and retrieving defect imagery based on visual similarity. The results of an industry field test of the ORNL image management system at two independent manufacturing sites are also described.
Using MAP estimation of feature transformation for speaker recognition

We propose to use a new feature transformation (FT) function to construct supervectors of support vector machines for speaker recognition. Considering that estimation of bias vectors is more robust than that of transformation matrices, we define the FT function in a flexible form that transformation matrices and bias vectors are controlled by separate regression classes. Unlike the MLLR-based approach that needs a continuous speech recognition system, our FT function parameters are estimated based on a Gaussian mixture model (GMM). An iterative training procedure is used to achieve the maximum a posteriori estimation of the FT function parameters, which avoids the possible numerical problem caused by insufficient training data in the maximum likelihood estimation. Our approach is evaluated on the SRE2006 NIST evaluation and obtains better performance than a conventional SVM system based on GMM mean supervectors.