Miracle-dz/UniFAR-FTUs huggingface dataset:
BdHCO-tree: integrating location privacy protection algorithms for frequent updates of moving objects

Together with the rapid development of location-based services (LBS), the problem of privacy-preserving has gained much attention. Many techniques aiming at protecting user's privacy have been introduced such as obfuscation-based technique, k-anonymity. Although existing solutions integrate location privacy protection algorithms into the indexed spatio-temporal data as Bob-tree and Bob+-tree, they do not conform to moving objects and naturally frequent updates. On the other hand, because of using the static order Hilbert curve, these indices usually have large size, and so decreasing query and update performance. To overcome such disadvantages, in this paper, we propose a BdHCO-tree that combines BdH-tree index, Hilbert Cloaking algorithm and obfuscation-based technique to enhance the performance of basic operations and increase the privacy protection. Besides, our solution is also integrated into spatio-temporal data index structure so that it can be used for commercial Database Management Systems transparently.
Learning heuristics for OKFDD minimization by evolutionary algorithms

Ordered Kronecker Functional Decision Diagrams (OKFDDs) are a data structure for efficient representation and manipulation of Boolean functions. OKFDDs are very sensitive to the chosen variable ordering and the decomposition type list, i.e. the size may vary from linear to exponential. In this paper we present an Evolutionary Algorithm (EA) that learns good heuristics for OKFDD minimization starting from a given set of basic operations. The difference to other previous approaches to OKFDD minimization is that the EA does not solve the problem directly. Rather, it develops strategies for solving the problem. To demonstrate the efficiency of our approach experimental results are given. The newly developed heuristics combine high quality results with reasonable time overhead.
Machine to Machine (M2M) Communications in Virtualized Vehicular Ad Hoc Networks

With the growing interest in the use of internet of things (IoT), machine-to-machine (M2M) communications have become an important networking paradigm. In this paper, with recent advances in wireless network virtualization (WNV), we propose a novel framework for M2M communications in vehicular ad-hoc networks (VANETs) with WNV. In the proposed framework, according to different applications and quality of service (QoS) requirements of vehicles, a hypervisor enables the virtualization of the physical vehicular network, which is abstracted and sliced into multiple virtual networks. Moreover, the process of resource blocks (RBs) selection and random access in each virtual vehicular network is formulated as a partially observable Markov decision process (POMDP), which can achieve the maximum reward about transmission capacity. The optimal policy for RBs selection is derived by virtue of a dynamic programming approach. Extensive simulation results with different system parameters are presented to show the performance improvement of the proposed scheme.
Conflicts and communication gaps in the intensive care unit

Purpose of reviewConflicts occur frequently in the ICU. Research on ICU conflicts is an emerging field, with only few recent studies being available on intrateam and team–family conflicts. Research on communication in the ICU is developing at a faster pace. Recent findingsRecent findings come from one multinational epidemiological survey on intrateam conflicts and one qualitative study on the causes and consequences of conflicts. Advances in research on communication with families in the ICU have improved our understanding of team–family and intrateam conflicts, thus suggesting targets for improvement. SummaryData about ICU conflicts depend on conflict definition, study designs (qualitative versus quantitative), patient case-mix, and detection bias. Conflicts perceived by caregivers are frequent and consist mainly in intrateam conflicts. The two main sources of conflicts in the ICU are end-of-life decisions and communication issues. Conflicts negatively impact patient safety, patient/family-centered care, and team welfare and cohesion. They generate staff burnout and increase healthcare costs. Further qualitative studies rooted in social-science theories about workplace conflicts are needed to better understand the typology of ICU conflicts (sources and consequences) and to address complex ICU conflicts that involve systems as opposed to people. Conflict prevention and resolution are complex issues requiring multimodal interventions. Clinical research in this field is insufficiently developed, and no guidelines are available so far. Prevention strategies need to be developed along two axes: improved understanding of family experience, preferences, and values, as well as evidence-based communication may reduce team–family conflicts and organizational measures including restoring leadership, multidisciplinary teamwork, and improved communication within the team may prevent intrateam conflicts in the ICU.
Evaluation of Remel Spectra CRE Agar for Detection of Carbapenem-Resistant Bacteria from Rectal Swabs Obtained from Residents of a Long-Term-Care Facility

ABSTRACT We compared the Remel Spectra CRE agar plate to CDC standard methodology for the isolation of carbapenem-resistant Enterobacteriaceae (CRE) from 300 rectal swab specimens obtained from patients residing in a long-term-care facility (LTCF). Multiplex PCR experiments were performed on isolates to identify specific Klebsiella pneumoniae carbapenemases (KPC) and additional β-lactamases. Of the 300 patients, 72 (24%) harbored CRE and were PCR positive for KPC enzymes. The Remel Spectra CRE plates detected KPC-type CRE in isolates from 70 of 72 patients (97.2%), while the CDC method detected CRE in 56 of 72 (77.8%). CRE identification results were available in 18 h compared to 36 h for the CDC method. Remel Spectra CRE agar plates can provide useful means for a fast and reliable method for detecting KPC-type CRE and for accelerated institution of appropriate infection control precautions.
ANALYSIS OF ECOTOURISM DEVELOPMENT OF SEMBILANGAN BEACH: CHARACTERS, FACTORS, AND CHALLENGES

Sembilangan beach in Bangkalan, East Java Province, Indonesia, is one of the potential and growing areas for ecotourism. This beach has several aspects of ecotourism development that has not been professionally managed to achieve ecotourism goals. The purpose of this study is to analyze the factors supporting the process of ecotourism development in Sembilangan beach, both in terms of visitors and the community. This knowledge is useful to support the strategic steps to accelerate its development. This research was conducted at Sembilangan beach, Bangkalan, East Java Province. Topics or the independent variables are explored related to the perception of tourists, culture, environmental conditions, community participation, economic conditions, and institutions. Data of the respondents are grouped according to their status: visitors and community. Simultaneous and Partial Test is used to understand the correlation. Environmental, economic and institutional factors have a simultaneous influence on community participation in the development of ecotourism. Environmental conditions partially have the greatest influence on community participation in the development of ecotourism Sembilangan beach
An efficient performance improvement method utilizing specialized functional units in Behavioral Synthesis

This paper proposes a novel behavioral synthesis method that improves performance of synthesized circuits utilizing specialized functional units effectively. Specialized functional units (e.g. multiply-accumulator) are designed for specific operation patterns to achieve shorter delay and/or smaller area than cascaded basic functional units. Almost all conventional methods cannot use specialized functional units effectively under a total area constraint because of their less flexibility for resource sharing. The proposed method makes it possible to solve module selection, scheduling, and functional unit allocation problems utilizing specialized functional units in practical time with some heuristics, and to reduce the number of clock cycles under total area and clock cycle time constraints. Experimental results show that the proposed method has achieved up to 35% and on average 14% reduction of the number of cycles with specialized functional units in practical time.
Characterizing and Classifying Card-Sharing Traffic through Wavelet Analysis

In the last years the interest in methods and techniques for circumventing digital video broadcasting security is continuously increasing, and digital TV content providers are struggling to restrict usage only to authorized users through complex conditional access systems. Currently, the most significant weakness is the card-sharing activity which allows a subscriber to provide access to digital contents to a group of users connected through an IP network. This is usually realized employing ad hoc customized devices. Detecting the presence of these illegal systems on the network by recognizing their related traffic is an important issue of primary importance. To avoid the identification of such traffic are often used payload obfuscation strategies based on encryption, making it difficult the adoption of packet inspection techniques. This paper presents some ideas about a possible strategy for binary classification and detection of card-sharing traffic based on the natural capability of Wavelet Analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas are a first step for the implementation of a classification scheme that relies only on time regularities of the traffic and not on the packet content that may be affected by protocol and payload obfuscation techniques.
Intracerebral Infusion of Glial Cell Line–Derived Neurotrophic Factor Promotes Striatal Neurogenesis After Stroke in Adult Rats

Background and Purpose— Stroke triggers increased progenitor proliferation in the subventricular zone (SVZ) and the generation of medium spiny neurons in the damaged striatum of rodents. We explored whether intrastriatal infusion of glial cell line–derived neurotrophic factor (GDNF) promotes neurogenesis after stroke. Methods— Adult rats were subjected to 2-hour middle cerebral artery occlusion (MCAO). GDNF was infused into the ischemic striatum either during the first week after MCAO, with the animals being killed directly thereafter, or during the third and fourth weeks, with the rats being killed 1 week later. New cells were labeled with 5′-bromo-2′deoxyuridine (BrdU) on day 7 or during the second week, respectively. Neurogenesis was assessed immunocytochemically with antibodies against BrdU and neuronal, glial, or progenitor markers. GDNF receptor expression was analyzed in SVZ tissue and neurospheres by reverse transcription—polymerase chain reaction and immunocytochemistry. Results— GDNF infusion increased cell proliferation in the ipsilateral SVZ and the recruitment of new neuroblasts into the striatum after MCAO and improved survival of new mature neurons. The GDNF receptor GFR&agr;1 was upregulated in the SVZ 1 week after MCAO and was coexpressed with markers of dividing progenitor cells. Conclusions— Intrastriatal infusion of GDNF in the postischemic period promotes several steps of striatal neurogenesis after stroke, partly through direct action on SVZ progenitors. Because delivery of GDNF has biological effects in the human brain, our data suggest that administration of this factor may promote neuroregenerative responses in stroke patients.
A Matrix Splitting Perspective on Planning with Options

We show that the Bellman operator underlying the options framework leads to a matrix splitting, an approach traditionally used to speed up convergence of iterative solvers for large linear systems of equations. Based on standard comparison theorems for matrix splittings, we then show how the asymptotic rate of convergence varies as a function of the inherent timescales of the options. This new perspective highlights a trade-off between asymptotic performance and the cost of computation associated with building a good set of options.
NMR in cancer. XI. Application of the NMR malignancy index to human gastro‐intestinal tumors

One hundred two specimens of human gastro‐instestinal tissue taken from eighty‐seven individuals were inspected by proton magentic resonance techniques (at 22.5 Megahertz). The purpose of the study was to evaluate the diagnostic capabilities of the nuclear magnetic resonance (NMR) technique with regard to the diagnosis of malignancy. The combination of two NMR parameters (spin‐lattice ((T1)) and spin‐spin ((T2)) relaxation times) into a malignancy index yielded complete discrimination between the two populations of tissue. The mean and standard deviations obtained were 2.004 ± 0.342 for normal tissue, and 3.266 ± 0.642 for malignant specimens. In addition, the NMR technique indicated that histologically normal tissue taken adjacent to the malignancy was pathologically „involved. ”︁ Analysis of the electrolyte and water content of such tissues confirms this abnormality.
Achievable Rate and Outage Probability of Cognitive Radio with Finite-Alphabet Inputs under Imperfect Spectrum Sensing

In this paper, we propose an effective method to calculate the average achievable rate and outage probability of a practical cognitive radio (CR) link with finite-alphabet inputs under imperfect spectrum sensing in fast and slow Rayleigh fading, respectively. In the considered CR system, the secondary user (SU) senses and dynamically exploits the spectrum pool via dynamic frequency hopping. Since spectrum sensing is not perfect, miss-detection occurs. Under this event, the interference emerged from collisions due to the simultaneous spectrum access of both primary and cognitive users leads to a non-Gaussian CR link. This makes it very challenging to evaluate the information theoretical limits, especially when finite-alphabet inputs are used. To overcome such challenge, we first introduce a simple method to calculate the instantaneous differential entropy of the channel output for a given fading gain using Laguerre-Gauss quadrature formulas. Using this result, we propose a piece-wise linear curve fitting (PLCF)-based method to calculate the average output entropy and outage probability, respectively. It is then demonstrated that the average achievable rate in fast fading and the outage probability in slow fading of the considered CR channel can be calculated effectively to achieve any predetermined accuracy level for a given finite-alphabet input.
Optimal allocation of thermal generating units through Shuffled Frog Leaping Algorithm

Most of the electric utilities have used an Integrated Resource Planning (IRP) tactic to spot the finest blend of demand and supply in order to certify system resource competence and consistency at a reasonable cost over a planning horizon. It is the best resource planning carried out by power utilities which take into account the supply-side and demand-side resources as a whole, so as to use them in an efficient, economic and rational way and under the principle of maintaining the energy service level to minimize the total cost of the plan. This paper presents a new loom for the solution of the economic load dispatch problem with a valve-paint effect using Shuffled Frog Leaping Algorithm. Evolutionary algorithms which are being used in many applications in the present decade, to have more optimized value in certain applications. The power system is one such application where the evolutionary algorithms are giving better results than conventional methods. Many meta-heuristic algorithms are available to provide a solution to the economic load dispatch problem. But they are lagging in computational time. In this paper, Shuffled Frog Leaping Algorithm (SFLA) is used to solve the ELD problem which provides optimized results in a fraction of a second. To reveal the effectiveness of the proposed approach, 3 generator system and 6 generator system are considered and the optimal values of power for each of the generator which gives minimum operating cost was found by this time effective Shuffled frog leaping algorithm.
FreezeOut: Accelerate Training by Progressively Freezing Layers

The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. Through experiments on CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets, a 20% speedup without loss of accuracy for ResNets, and no improvement for VGG networks. Our code is publicly available at this https URL
Mutual coupling between adjacent faces of a four-sided MPAR array

Our team has performed trade and design studies for the FAA and NOAA of multifunction phased array radar (MPAR) concepts that would combine the functionalities of air traffic control, air surveillance, precision weather observation, and detection of clear air turbulence in a single radar unit. Such a system requires multiple independently operating sectors to perform all functions within the required timeline. Our study identified a pyramidal building with four phased array faces as an attractive geometry, in part because faces are isolated from each other by the structure and corners, permitting reception on one face while simultaneously transmitting on its neighbors. We report here on an initial risk-reduction investigation involving simulations that characterized the achievable face-to-face isolation. We conclude that an MPAR design based on a four-faced pyramidal structure should provide sufficient isolation to allow simultaneous and independent operation and tasking of adjacent faces.
Timing anomalies in dynamically scheduled microprocessors

Previous timing analysis methods have assumed that the worst-case instruction execution time necessarily corresponds to the worst-case behavior. We show that this assumption is wrong in dynamically scheduled processors. A cache miss, for example, can in some cases result in a shorter execution time than a cache hit. Many examples of such timing anomalies are provided. We first provide necessary conditions when timing anomalies can show up and identify what architectural features that may cause such anomalies. We also show that analyzing the effect of these anomalies with known techniques results in prohibitive computational complexities. Instead, we propose some simple code modification techniques to make it impossible for any anomalies to occur. These modifications make it possible to estimate WCET by known techniques. Our evaluation shows that the pessimism imposed by these techniques is fairly limited; it is less than 27% for the programs in our benchmark suite.
Bandwidth enhancement of planar EBG structure using dissipative edge termination

In this paper, the bandwidth enhancement of planar type electromagnetic bandgap (EBG) structure using dissipative edge termination technique is investigated. Two termination schemes are presented; first with the termination elements along edges of every single EBG patches and second with termination along edges of the whole power plane. In the first case, simulations show that, both lower and upper cutoff frequency is improved for wideband suppression of simultaneous switching noise (SSN). For the second one, lower cutoff frequency is shifted downward from 800MHz of common planar EBG structure to 13MHz. which results in relative bandwidth of 63% higher than the commonly used EBGs. For fast simulation and validation of full-wave simulation, the circuit model of these structures is developed and simulation results are also presented.
Application of an approximate model predictive control scheme on an unmanned aerial vehicle

An approximate model predictive control approach is applied on an unmanned aerial vehicle with limited computational resources. A novel method using a continuous time parametrization of the state and input trajectory is used to derive a compact description of the optimal control problem. Different first order methods for the online optimization are discussed in terms of memory requirements and execution time. The generalized fast dual gradient method is implemented on the aerial vehicle. The approximate model predictive control algorithm runs on an embedded platform with a STM32 Cortex M4 processor. Simulation studies show that the model predictive controller outperforms a linear quadratic regulator in aggressive maneuvers. The model predictive control approach is evaluated in practice and shown to yield satisfactory flight behavior.
Bounded conditional mean imputation with Gaussian mixture models: A reconstruction approach to partly occluded features

In this work we show how conditional mean imputation can be bounded through the use of box-truncated Gaussian distributions. That is of interest when signals or features are partly occluded by a superimposed interference, as then the noisy observation poses an upper bound. Unfortunately, the occurring integrals are not analytic. Hence an approximate solution has to be used. In the experimental section we apply the bounded approach to the reconstruction of partly occluded speech spectra and demonstrate its superiority over the unbounded case with respect to automatic speech recognition performance.
Frameworks for Information Exploration - A Case Study

The exploration of information has become a common task among data consumers, leveraged mostly by the increasing availability of (semi) structured data on the Web. Even though there has been much research on data visualization techniques to support sense making over large datasets, the design and development of tools to support exploration actions with sufficient expressivity still lacks a systematic approach. In this paper we present a case study as a means to argue the relevance of having a model of operations as approach to address expressivity issues in exploration tools. The case study demonstrates the value of a framework of operations not only for tool comparisons, but also for representing, describing, and leveraging sharing of exploration patterns among communities of users.
Modularity Matters Most

We contend that modularity is the key to improving software quality. We advocate a view of modularity that emphasizes not the mere assembling of software systems from component parts, but rather the specification of interfaces between components, verification that components meet their specifications, and the assembling only of components with compatible specifications. Key to this methodology is the use of types to specify and automatically to verify adherence to interfaces. We claim that this methodology makes a higher degree of software correctness possible than has often been achieved heretofore, and moreover, that it may be achieved in a practical manner. To reach this goal will require the development of sophisticated new type system, will require new techniques for modularizing certain correctness properties, and will require a delicate balance between concise code and automated checking. ∗E-mail: crary@cs.cmu. edu (Corresponding author.) †E-mail: rwh@cs.cmu. edu. ‡E-mail: petel@cs.cmu. edu §E-mail: fp@cs.cmu. edu