Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable IP-VPN Flow Control Mechanism Supporting Arbitrary Fairness Criteria - Part 2: Simulation and Implementation

In recent years, IP-based virtual private networks (IP-VPNs), which provide a virtual privately owned network over an IP network, have attracted attention. With existing IP-VPNs, however, there is a serious problem that fairness among IP-VPN customers is not satisfied. In our previous work, we have proposed an IP-VPN fairness control mechanism called I2VFC (Inter-and Intra-VPN Fairness Control) that realizes fairness among IP-VPN customers. In this paper, we quantitatively show effectiveness of our I2VFC using simulation experiments and prototype system experiments. Focusing on inter-VPN fairness, intra-VPN fairness, and scalability, we extensively analyze the performance of I2VFC. Consequently, we show that I2VFC can realize both inter-and intra-VPN fairness under diverse control parameter configurations, indicating robustness and parameter insensitivity of our I2VFC. We also show that I2VFC has a practically sufficient scalability in terms of the transfer speed and the number of VPNs accommodated. For instance, measurement results using our prototype system show that with a modern desktop computer, I2VFC can support approximately 1.6 [Gbit/s] bandwidth and 1,300 numbers of VPNs.
Evaluation of copper nanoparticles for low temperature bonded interconnections

In this study, we evaluate the application of copper nanoparticles as a low temperature bonded interconnection. The copper nanoparticles have an organic passivation layer which stabilizes them, preventing spontaneous particle fusion and growth at ambient temperatures, as well as avoiding oxidation before its usage. Thermogravimetric analysis and differential scanning calorimetry show that the solvents and passivation layer can be removed completely from the copper nanoparticles paste by a sintering profile of up to 200°C. Cross sectional analysis show that the copper nanoparticles have fused to create a low porosity layer that gave good shear strength of the bonded joint. Moreover, Cu nanoparticles bonded samples demonstrate excellent thermal stability after 1000 h of aging at 150°C under normal ambient, as the shear strength of the joints increased instead without any observable oxidation of the Cu nanoparticles layer. Lastly, the addition of copper nanowires to the nanoparticles paste further improves the mechanical strength of the bonded interconnections due to the retardation of the crack propagation.
Effectiveness of Brief DBT-informed Group Therapy on Psychological Resilience: A Preliminary Naturalistic Study

ABSTRACT This naturalistic study was designed to examine the effectiveness of a 4-week skill-based group therapy that focuses on building resilience. The group, informed by Dialectical Behavior Therapy (DBT; Linehan, 1993, 2015), targets general clients at college counseling centers and emphasizes mindfulness as a coping strategy to stress, cognitive rigidity, emotion dysregulation, and interpersonal difficulties. Results indicated that group members showed clinically significant improvement in psychological resilience and general mental health at the conclusion of group therapy. Implications are discussed.
Using synthetic clinical data to train an HMM-based POS tagger

The accuracy of part of speech (POS) tagging reported in medical natural language processing (NLP) literature is typically very high when training and testing data sets are from the same domain and have similar characteristics, but is lower when these differ. This presents a problem for clinical NLP, where it is difficult to obtain large corpora of training data suitable for localized tasks. We experimented with implementing the TnT POS tagger and training it on a manually tagged small corpus of publicly available synthetic clinical reports supplemented with widely used public corpora (GENIA and Penn Treebank). We describe this implementation and report the evaluation results on MiPACQ, a large corpus of manually tagged clinical text. Our tagger achieves accuracy comparable to POS taggers trained on large amounts of real clinical data (91-93%). This demonstrates that medical NLP developers do not need to rely on large restricted resources for POS tagging.
Approximating Free‐form Geometry with Height Fields for Manufacturing

We consider the problem of manufacturing free‐form geometry with classical manufacturing techniques, such as mold casting or 3‐axis milling. We determine a set of constraints that are necessary for manufacturability and then decompose and, if necessary, deform the shape to satisfy the constraints per segment. We show that many objects can be generated from a small number of (mold‐)pieces if slight deformations are acceptable. We provide examples of actual molds and the resulting manufactured objects.
Type and Proximity of Green Spaces Are Important for Preventing Cardiovascular Morbidity and Diabetes—A Cross-Sectional Study for Quebec, Canada

This study aimed at determining the role of proximity to specific types of green spaces (GSes) as well as their spatial location in the relationship with the most morbid cardiovascular diseases (CVD) and diabetes. We measured the accessibility to various types of GS and used a cross-sectional approach at census Dissemination Area (DA) levels in the Montreal and Quebec City metropolitan zones for the period 2006–2011. Poisson and negative binomial regression models were fitted to quantify the relationship between distances to specific types of GS and CVD morbidity as well as some risk factors (diabetes and hypertension) while controlling for several social and environmental confounders. GSes that have sports facilities showed a significant relationship to cerebrovascular diseases: the most distant population had an 11% higher prevalence rate ratio (PRR) compared to the nearest, as well as higher diabetes risk (PRR 9%) than the nearest. However, the overall model performance and the understanding of the role of GSes with sport facilities may be substantially achieved with lifestyle factors. Significantly higher prevalence of diabetes and cerebrovascular diseases as well as lower access to GSes equipped with sports facilities were found in suburban areas. GSes can advantageously be used to prevent some CVDs and their risk factors, but there may be a need to reconsider their types and location.
Critical sampling period for an open-loop perturband-observe (P&O) maximum power point tracking (MPPT) grid-connected solar photovoltaic (PV) system

The critical sampling period depends on the natural angular frequency of the system, but it is very difficult to obtain for a real system. By modeling and simulation of the PV and DC-DC converter modules in the MATLAB/SIMULINK environment, the critical sampling period for an open-loop P&O MPPT grid-connected solar PV system has been determined, and the corresponding optimum percentage overshoot and settling time. Data from a real world 2-MWp grid-connected solar PV system of Volta River Authority (VRA) of Ghana were used for the study.
Locally regressive G-optimal design for image retrieval

Content Based Image Retrieval (CBIR) has attracted increasing attention from both academia and industry. Relevance Feedback is one of the most effective techniques to bridge the semantic gap in CBIR. One of the key research problems related to relevance feedback is how to select the most informative images for users to label. In this paper, we propose a novel active learning algorithm, called Locally Regressive G-Optimal Design (LRGOD) for relevance feedback image retrieval. Our assumption is that for each image, its label can be well estimated based on its neighbors via a locally regressive function. LRGOD algorithm is developed based on a locally regressive least squares model which makes use of the labeled and unlabeled images, as well as simultaneously exploits the local structure of each image. The images that can minimize the maximum prediction variance are selected as the most informative ones. We evaluated the proposed LRGOD approach on two real-world image corpus: Corel and NUS-WIDE-OBJECT [5] datasets, and compare it to three state-of-the-art active learning methods. The experimental results demonstrate the effectiveness of the proposed approach.
The Impact of Mutation and Gene Conversion on the Local Diversification of Antigen Genes in African Trypanosomes

Patterns of genetic diversity in parasite antigen gene families hold important information about their potential to generate antigenic variation within and between hosts. The evolution of such gene families is typically driven by gene duplication, followed by point mutation and gene conversion. There is great interest in estimating the rates of these processes from molecular sequences for understanding the evolution of the pathogen and its significance for infection processes. In this study, a series of models are constructed to investigate hypotheses about the nucleotide diversity patterns between closely related gene sequences from the antigen gene archive of the African trypanosome, the protozoan parasite causative of human sleeping sickness in Equatorial Africa. We use a hidden Markov model approach to identify two scales of diversification: clustering of sequence mismatches, a putative indicator of gene conversion events with other lower-identity donor genes in the archive, and at a sparser scale, isolated mismatches, likely arising from independent point mutations. In addition to quantifying the respective probabilities of occurrence of these two processes, our approach yields estimates for the gene conversion tract length distribution and the average diversity contributed locally by conversion events. Model fitting is conducted using a Bayesian framework. We find that diversifying gene conversion events with lower-identity partners occur at least five times less frequently than point mutations on variant surface glycoprotein (VSG) pairs, and the average imported conversion tract is between 14 and 25 nucleotides long. However, because of the high diversity introduced by gene conversion, the two processes have almost equal impact on the per-nucleotide rate of sequence diversification between VSG subfamily members. We are able to disentangle the most likely locations of point mutations and conversions on each aligned gene pair.
On the Existence of Pure Strategy Nash Equilibrium for Non-cooperative Games in L-convex Spaces

In the setting of L-convex spaces, this paper proves a result ensuring the existence of pure strategy Nash equilibrium for non-cooperative games with infinite countable players. Following the method introduced by Nikaido and Isoda (1955), this paper defines an aggregate payoff function, and next then, determines some restrictions on the aggregate function that guarantee the existence of pure strategy Nash equilibrium for non-cooperative games with infinite countable players. In the process of proof, the method adopted by this paper is to apply the continuous unity partition theorem and a famous fixed point theorem due to Górniewicz (1975). Finally, some new perturbed saddle point theorems are obtained. Our results generalize and improve the known Nash equilibrium existence results for non-cooperative games with finite players in the literature. Our results improve and unify the corresponding results in the recently existing literatures.
IRI-TR-1003 Path Planning with Pose SLAM

The probabilistic belief networks that result from standard feature-based simultaneous localization and map building (SLAM) approaches cannot be directly used to plan trajectories. The reason is that they produce a sparse graph of landmark estimates and their probabilistic relations, which is of little value to find collision free paths for navigation. In contrast, we argue in this paper that Pose SLAM graphs can be directly used as belief roadmaps (BRMs). The original BRM algorithm assumes a known model of the environment from which probabilistic sampling generates a roadmap. In our work, the roadmap is built on-line by the Pose SLAM algorithm. The result is a hybrid BRM-Pose SLAMmethod that devises optimal navigation strategies on-line by searching for the path with lowest accumulated uncertainty for the robot pose. The method is validated over synthetic data and standard SLAM datasets. Institut de Robòtica i Informàtica Industrial (IRI) Consejo Superior de Investigaciones Cientı́ficas (CSIC) Universitat Politècnica de Catalunya (UPC) Llorens i Artigas 4-6, 08028, Barcelona, Spain Tel (fax): +34 93 401 5750 (5751) http://www.iri.upc.edu Corresponding author: Rafael Valencia tel: +34 93 4015780 rvalencia@iri.upc.edu http://www.iri.upc.edu/people/ rvalencia
Applicability of the SECI model of knowledge creation in Russian cultural context: theoretical analysis

While the potential influence of national culture on the efficiency of knowledge management interventions has been widely accepted, the question of whether the knowledge management theories are influenced by culture received little attention. This paper aims to address this gap by analyzing SECI model of knowledge creation by Nonaka and Takeuchi in the context of Russian national culture. The model's authors claim of its universal validity, and current criticism against this position is limited and controversial. We maintain that it is partly due to the fact that SECI model in its original format resists empirical verification. Thus we propose a framework for operationalization of the SECI model. We suggest that decomposing SECI model into three levels—basic cognitive processes, societal and organizational conditions, and managerial tools—opens the ways for its empirical and cross-cultural investigation. Applying this framework to Russia, we develop a set of theoretical propositions regarding opportunities and limits of SECI application in Russian cultural context. Finally we discuss theoretical and management implications of our analysis. Copyright © 2010 John Wiley & Sons, Ltd.
Using Structured UKR manifolds for motion classification and segmentation

Task learning from observations of non-expert human users will be a core feature of future cognitive robots. However, the problem of task segmentation has only received minor attention. In this paper, we present a new approach to classifying and segmenting series of observations into a set of candidate motions. As basis for these candidates, we use Structured UKR manifolds, a modified version of Unsupervised Kernel Regression which has been introduced in order to easily reproduce and synthesise represented dextrous manipulation tasks. Together with the presented mechanism, it then realises a system that is able both to reproduce and recognise the represented motions.
Using Kademlia for the configuration of B3G radio access nodes

Configuration and management of radio access nodes in beyond 3G (B3G) networks such as nodeBs, wireless LAN or WIMAX access points is a complex task that consumes a high amount of resources. Most configuration changes have to be done by an operator, especially when it comes to the adaption of a node to its environment, i.e., the cells surrounding its own. This is an expensive and inflexible method. Moreover, the required manual interference hampers the adaptivity of B3G networks towards topology changes. In this work, we present an architecture that allows for the detection of neighboring cells, thereby enabling an easy or even autonomic configuration of the access nodes. In addition to this, the presented Kademlia-based overlay network enables the storage of information needed for network management, such as configuration data or performance measures
The Involvement of a Polyphenol-Rich Extract of Black Chokeberry in Oxidative Stress on Experimental Arterial Hypertension

The aim of this study is to characterize the content of Aronia melanocarpa Elliott (black chokeberry) extract and also to estimate the influence of polyphenolic compounds contained in chokeberries on oxidative stress, on an L-NAME-induced experimental model of arterial hypertension. The rat blood pressure values were recorded using a CODA Noninvasive Blood Pressure System. HPLC/DAD coupled with ElectroSpray Ionization-Mass Spectrometry allowed identification of five phenolic compounds in berries ethanolic extract as follows: chlorogenic acid, kuromanin, rutin, hyperoside, and quercetin. The serous activity of glutathione-peroxidase (GSH-Px) has significantly lower values in the hypertensive (AHT) group as compared to the group protected by polyphenols (AHT + P). The total antioxidant capacity (TAC) values are lower in the AHT group and they are significantly higher in the AHT + P group. All the measured blood pressure components revealed a biostatistically significant blood pressure drop between the AHT group and the AHT + P group. The results reveal the normalization of the reduced glutathion (GSH) concentration as well as a considerable reduction in the malondialdehyde (MDA) serum concentration in the AHT + P group. Ethanolic extract of black chokeberry fruits not only has a potential value as a prophylactic agent but also may function as a nutritional supplement in the management of arterial hypertension.
Ultra Low Power Wireless Weather Station

Currently the appearance of really low power wireless transceivers at very low prices is motivating the development of many wireless applications out of the industrial field, which up to now turn to be large in size and expensive. We present the design of a tiny and low cost Wireless Weather Station to measure accurate temperature (plusmn0.1degC), relative humidity (plusmn3%), light intensity and atmospheric pressure (plusmn0.8 hPa). These direct climatic variables and others indirectly attainable, like the dew-point, wind chill, etc, are readable through a Web page. The chosen sensors are factory calibrated and have a digital interface. The Weather Sensor Nodes are able to achieve ultra low power consumption (40 muA average), allowing a single supercapacitor to power them for 52 days.
The Hierarchies and Systems That Underlie Routine Behavior: Evidence from an Experiment in Virtual Gardening

Previous behavioral research into the processing mechanisms that support action selection in complex sequential tasks has focused on errors or ‘slips of action’ (Norman, 1981; Reason, 1979, 1984). This work has been taken to suggest that sequential behavior is controlled by the dynamic interaction of two systems – a resource hungry “supervisory” system and an automatic “routine” system that employs hierarchically structured task representations (Norman & Shallice, 1986). To counter some of the shortcomings of purely error-based natural history studies we developed an alternative methodology which enables more controlled, direct and finegrained investigation of the underlying processes, namely the use of action selection latencies within novel, computerbased, tasks. While the methodology is relatively general, the primary aim of the specific experiment reported here was to test the hypotheses that routine behavior is governed by (a) hierarchically structured task representations, and (b) the dynamic interaction of two systems. The experiment was additionally designed to investigate the impact of several factors on the processing difficulty of specific action selection steps, including experience, number of choices, availability of external disambiguation cues, and minor variations within subsequences. Our results provide some direct support for the Dual Systems framework, but also pose challenges with regard to further refinements and computational models.
Indra Ganesan College of Engineering MONITORING INTERACTIONS WITH RFID TAGGED OBJECTS USING RSSI

-In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. Passive RFID is an emerging technology allowing identification of devices, called tags, when interrogated by RFID readers. The Ultra High Frequency (UHF) RFID readers with identical emission configuration attached to a mobile robot to identify a reference RFID tag, using UHF readers it is possible to read multiple tags at a time. The particular product number can be entered into the mobile robot from the control room itself using Zigbee. By utilizing the Received Signal Strength Indicator (RSSI) obtained by the readers from the reference RFID tag, the precise position of the mobile robot can be obtained. The obstacle in the moving path of the mobile robot can be detected using obstacle sensors and redirects its path, by indicating an alarm signal. This method can be used in real industry environments such as complex storage warehouses where many different goods are dispersed throughout a wide area.
Knowledge of General Dentists and Senior Dental Students in Iran about Prevention of Infective Endocarditis

Infective endocarditis (IE) is an infection of the endothelial surface of the heart and heart valves with serious, even fatal, complications and that often requires long-term and expensive treatment. Dental procedures may lead to IE in high-risk patients. The aim of this study was to evaluate and compare the knowledge of general dentists and dental students concerning the prevention of IE in Hamadan, Iran. In this cross-sectional study, the awareness of general dentists and dentistry students concerning the prevention of IE was evaluated during 2010. A questionnaire was prepared and administered to 58 final-year dental students and 96 general dental practitioners in Hamadan. A total of 154 persons completed the questionnaire. The questionnaire consisted of some demographic questions and questions about awareness of IE in three sections. The gathered data were analyzed by using descriptive statistics, Pearson's chi-square test, Mann-Whitney test, and independent t-tests. The gathered data showed that dentistry students answered the questions about awareness of the prevention of IE more correctly than did general dentists. The overall knowledge of endocarditis prophylaxis among students and dentists was about 65% and 56%, respectively. The students' knowledge was better because 94.9% of the students had desired (acceptable) and relatively desired knowledge; this result for dentists, however, was 82.3%. In our study, the overall awareness level of the study population was moderate. Dentist and students believed that patients with prosthetic valves and previous IE were the most common cardiac disease cases that required prophylaxis. The most common prophylactic regimen was in accordance with the guidelines of the American Heart Association and was a single dose of 2 g amoxicillin 1 hour before treatment. The results indicated that gender had no effect on the level of knowledge; however, there was a statistically significant relationship between age and level of knowledge.