Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing an Efficient and Searchable Encrypted Log Using Record Authenticator

Audit logs are very important part of any secure system and should be highly considered. The content of them which is past events is so sensitive and poses a threat to privacy and information security, so should be protected against any illegal reading and modification. The best way to do this is encryption. Important challenges in an encrypted audit log are speed of log and search process, correctness and semantic of search results and log size. In this paper a novel algorithm in order to build a secure searchable encrypted audit log by generating a record authenticator is presented. SQL command keywords along with their places, form record authenticator. Our new algorithm brings remarkable advantages such as: fast and high accurate search without decrypting any record, fast logging process, keeping tamper resistance of the log, decreasing log size and improving the correctness of search results by recognizing all irrelevant records in search time.
Evaluation of the Bioactivity of Novel Spiroisoxazoline Type Compounds against Normal and Cancer Cell Lines

The aim of this study was to investigate the in vitro cellular activity of novel spiroisoxazoline type compounds against normal and cancer cell lines from lung tissue (Hs888Lu), neuron-phenotypic cells (SH-SY5Y), neuroblastoma (SH-SY5Y), human histiocytic lymphoma (U937), lung cancer (A549), and leukaemia (HL-60). Our bioassay program revealed that the spiroisoxazoline type compounds show cytotoxicity only in lymphoma cell lines, which is in contrast with the pyrrolidine precursor of these spiroisoxazoline compounds, where significant cytotoxicity is seen in all normal and cancer cell lines. These data suggest a tumour-specific mechanism of action. In addition these data also show that spiroisoxazoline compounds are non-toxic in the human neuron-phenotypic neuroblastoma SH-SY5Y cell line, and furthermore that they might protect cells from neurodegenerative disease.
A Vision on Services and Architectures for 4G

In this paper we will motivate the cellular controlled short-range communication for cooperative services in 4G wireless networks. Without going into the technical details, we advocate by means of very illustrative scenarios, that cooperation of users and their terminals better services can be achieved at lower prices. This new step in technology comes in combination with a social aspects of cooperation between customers.
Metabolic fuels: regulating fluxes to select mix

Summary Animals must regulate the fluxes of multiple fuels to support changing metabolic rates that result from variation in physiological circumstances. The aim of fuel selection strategies is to exploit the advantages of individual substrates while minimizing the impact of disadvantages. All exercising mammals share a general pattern of fuel selection: at the same they oxidize the same ratio of lipids to carbohydrates. However, highly aerobic species rely more on intramuscular fuels because energy supply from the circulation is constrained by trans-sarcolemmal transfer. Fuel selection is performed by recruiting different muscles, different fibers within the same muscles or different pathways within the same fibers. Electromyographic analyses show that shivering humans can modulate carbohydrate oxidation either through the selective recruitment of type II fibers within the same muscles or by regulating pathway recruitment within type I fibers. The selection patterns of shivering and exercise are different: at the same , a muscle producing only heat (shivering) or significant movement (exercise) strikes a different balance between lipid and carbohydrate oxidation. Long-distance migrants provide an excellent model to characterize how to increase maximal substrate fluxes. High lipid fluxes are achieved through the coordinated upregulation of mobilization, transport and oxidation by activating enzymes, lipid-solubilizing proteins and membrane transporters. These endurance athletes support record lipolytic rates in adipocytes, use lipoprotein shuttles to accelerate transport and show increased capacity for lipid oxidation in muscle mitochondria. Some migrant birds use dietary omega-3 fatty acids as performance-enhancing agents to boost their ability to process lipids. These dietary fatty acids become incorporated in membrane phospholipids and bind to peroxisome proliferator-activated receptors to activate membrane proteins and modify gene expression.
Options Pricing with Arithmetic Brownian Motion and its Implication for Risk-Neutral Valuation

Risk-neutral valuation is used widely in derivatives pricing. It is shown in this paper, however, that the naive approach of simply setting the growth rate of the underlying security to risk-free interest rate, which happens to work for a geometric Brownian motion (GBM) process, fails to work when the underlying price follows the arithmetic Brownian motion (ABM). Therefore, the formal approach using a martingale measure should be used instead when the underlying process is not a GBM.
Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting

Purpose Image-based computational models with fluid-structure interaction (FSI) can be used to perform plaque mechanical analysis in intracranial artery stenosis. We described a process in FSI study applied to symptomatic severe intracranial (M1) stenosis before and after stenting. Materials and Methods Reconstructed 3D angiography in STL format was transferred to Magics for smoothing of vessel surface and trimming of branch vessels and to HyperMesh for generating tetra volume mesh from triangular surface-meshed 3D angiogram. Computational analysis of blood flow in the blood vessels was performed using the commercial finite element software ADINA Ver 8.5. The distribution of wall shear stress (WSS), peak velocity and pressure was analyzed before and after intracranial stenting. Results The wall shear stress distributions from Computational fluid dynamics (CFD) simulation with rigid wall assumption as well as FSI simulation before and after stenting could be compared. The difference of WSS between rigid wall and compliant wall model both in pre- and post-stent case is only minor except at the stenosis region. These WSS values were greatly reduced after stenting to 15~20 Pa at systole and 3~5 Pa at end-diastole in CFD simulation, which are similar in FSI simulations. Conclusion Our study revealed that FSI simulation before and after intracranial stenting was feasible despite of limited vessel wall dimension and could reveal change of WSS as well as flow velocity and wall pressure.
International Conference on Computational Science and Technology – 2014 ( ICCST ’ 14 ) Automated Face Annotation for Personal Photo Management

Automatic face annotation is a process of assigning words or names to human face photos. With the rapid growth of digitized photos captured for our personal collections, album management becomes very cumbersome. Commercial image organizer products such as ACDSee, F-Spot and Picasa allow users to automatically group images by folder, album, and date; thus easing browsing and retrieval. However, a study showed that users usually tend to organize images into collections by date, location, and events. Therefore, we proposed an automated face annotation using face features, clothing colours and date the photo is taken. A total of 175 photographs comprising 138 frontal faces are selected from events such as Eid celebration, birthday celebration and outdoor occasions. The face features are described using eigenfaces, while the clothing colours are represented using La*b* colour histogram. Julian date format is added into the feature vector and metric-distance similarity measure is utilized to recognize the face. A recognition rate of over eighty percent is achieved for our proposed method. Keywords-face annotation; eigenfaces; colour histogram; distance metric
Rationed Access and Welfare: The Case of Public Resource Lotteries

Pressures on public lands and waterways are resulting increasingly in the rationing of public access by lottery. Upon accounting for the uncertainties of random rationing, discrete choice models lend themselves to analyzing participation in public resource lotteries and estimating welfare changes. Key to valuing lotteryrationed rights is accounting for changes in access probabilities that result from policy changes. The empirical application models the discrete choices of more than 18,000 participants in a New Mexico lottery system for elk harvest rights. Welfare estimates are obtained from simulated policy changes that affect, individually and jointly, the access probability and indirect utility. (JEL Q20)
Data mining as a tool for hot day prediction during summer monsoon

Human health, energy, and comfort are affected more by climate than by any other element of the physical environment. Physiological functions of the human body respond to changes in the weather with climate and the seasons. The emphasis of present work is to provide the hot day temperature patterns during April and May months and extract its behavioral aspects using association rule mining of data mining technique. This study describes that the proposed learning mechanism can predict the occurrence of hot day during April and May months over the coastal station Cuddalore (Latitude 11°46' N / Longitude 79°46' E) of south east India. The proposed model finds the extreme hot day prediction 24 hours ahead and 48 hours ahead with the help of local weather parameters.
Inventory Effects on Daily Returns in Financial Markets

In this paper we investigate the properties of daily returns arising from inventory effects. We therefore use the well established framework of inventory-based models from market microstructure theory. It is shown using simulation studies that from this model daily returns exhibit excess volatility, negative first-order autocovariances and the volatility has a positive first-order autocovariance, which is consistent with a GARCH-process. An empirical investigation shows that a substantial part of the properties of daily returns in stock market data can be explained by inventory effects.
Analytical torque model for a permanent magnet spherical motor using magnetic field reconstruction from measured boundary conditions

This paper presents an analytical method for reconstructing the magnetic flux density (MFD) field around the rotor of a permanent magnet spherical motor (PMSM) using measured MFD on its surface, and for formulating the motor torque according to Lorentz's law. When reconstructing the MFD, the solutions to Laplace's equation of a magnetic scalar potential with measured boundary conditions (BCs) in the current-free space around the rotor are obtained using the method of separation of variables. The models yield precise MFD and torque solutions in closed form, and yet require no information on the magnetic structures and properties of the rotor. This advantage provides a good basis for model identification, calibration as well as for control of an electromagnetic actuator. Both the magnetic field and torque models have been validated by comparing against published experimental data, which show excellent agreement.
Context-Sensitive Inference Rule Discovery: A Graph-Based Method

Inference rule discovery aims to identify entailment relations between predicates, e.g., ‘X acquire Y –> X purchase Y’ and ‘X is author of Y –> X write Y’. Traditional methods dis-cover inference rules by computing distributional similarities between predicates, with each predicate is represented as one or more feature vectors of its instantiations. These methods, however, have two main drawbacks. Firstly, these methods are mostly context-insensitive, cannot accurately measure the similarity between two predicates in a specific context. Secondly, traditional methods usually model predicates independently, ignore the rich inter-dependencies between predicates. To address the above two issues, this pa-per proposes a graph-based method, which can discover inference rules by effectively modelling and exploiting both the context and the inter-dependencies between predicates. Specifically, we propose a graph-based representation—Predicate Graph, which can capture the semantic relevance between predicates using both the predicate-feature co-occurrence statistics and the inter-dependencies between predicates. Based on the predicate graph, we propose a context-sensitive random walk algorithm, which can learn con-text-specific predicate representations by distinguishing context-relevant information from context-irrelevant information. Experimental results show that our method significantly outperforms traditional inference rule discovery methods.
Just-in-Time Adaptive Algorithm for Optimal Parameter Setting in 802.15.4 WSNs

Recent studies have shown that the IEEE 802.15. 4 MAC protocol suffers from severe limitations, in terms of reliability and energy efficiency, when the CSMA/CA parameter setting is not appropriate. However, selecting the optimal setting that guarantees the application reliability requirements, with minimum energy consumption, is not a trivial task in wireless sensor networks, especially when the operating conditions change over time. In this paper we propose a Just-in-Time LEarning-based Adaptive Parameter tuning (JIT-LEAP) algorithm that adapts the CSMA/CA parameter setting to the time-varying operating conditions by also exploiting the past history to find the most appropriate setting for the current conditions. Following the approach of active adaptive algorithms, the adaptation mechanism of JIT-LEAP is triggered by a change detection test only when needed (i.e., in response to a change in the operating conditions). Simulation results show that the proposed algorithm outperforms other similar algorithms, both in stationary and dynamic scenarios.
Calorie Labeling in a Rural Middle School Influences Food Selection: Findings from Community-Based Participatory Research

Background. Calorie labeling at the point-of-purchase in chain restaurants has been shown to reduce energy intake. Objective. To investigate the impact of point-of-purchase calorie information at one rural middle school. Methods. With a community-based participatory research framework a mixed method approach was used to evaluate the impact of point-of-purchase calorie information. Students in grades 6–8, dining at the school cafeteria January and February 2010, participated for 17 school days each month; in January a menu was offered in the usual manner without calorie labels; the same menu was prepared in February with the addition of calorie labels at point-of-purchase. Gross calories served per student were measured each day allowing for matched comparison by menu. In March/April of 2010, 32 students who ate in the cafeteria 3 or more times per week were interviewed regarding their views on menu labeling. Results. Calorie consumption decreased by an average of 47 calories/day; fat intake reduced by 2.1 grams/day. Five main themes were consistent throughout the interviews. Conclusion. Point-of-purchase calorie labels can play a role in reducing the number of calories consumed by middle school age children at the lunch. The majority of students interviewed found the calorie labels helped them choose healthier food.
In Vitro and in Vivo Effect of Adriamycin Therapy on Monocyte Activation by Liposome-encapsulated Immunomodulators 1

The purpose of these studies was to determine the effect of Adriamycin (ADR) on the ability of liposome-encapsulated immunomodulators to activate human blood monocytes to the tumoricidal state. We undertook these experiments because we envisioned using encapsulated activators in addition to chemotherapy to destroy pulmonary micrometastases in patients with osteosarcoma (OS). Prior to the initiation of such therapy, it was important to determine whether chemotherapy interferes with monocyte function. First, human peripheral blood monocytes were iso lated from normal donors and preincubated with ADR (0.5-500 ng/ml) for I h and then washed prior to the addition of free or liposomeencapsulated activators. After 18-24 h incubation, the activating agents were washed off and |l25I]IdUrd-labeled A375 melanoma cells were added. Lysis of radiolabeled tumor cells was quantified 72 h later. Monocytes were also incubated with ADR for 24 h in the presence of free or liposomeencapsulated activators and their cytotoxicity quantified. ADR had no effect on the ability of either free or liposome-encapsulated agents to activate monocyte tumoricidal function. We also studied the in vivoeffect of ADR therapy on monocyte function in nine patients with OS. At the time of diagnosis and 1 month after ADR therapy (75 mg/m2) patient monocytes could be activated to the tumoricidal state by liposome-encapsulated agents at levels equal to or greater than pretherapy levels. Monocytes isolated from four patients with OS I day after ADR therapy and then activated by liposomeencapsulated agents also demonstrated tumoricidal activity. These studies indicated that the monocytes isolated from osteosarcoma patients treated with ADK can be activated in vitro to kill tumor cells and that additional therapy with liposome-encapsulated immunomodulators may be com bined with ADR in the treatment of metastatic pulmonary OS.
Prototyping a software factory for wireless sensor networks

Wireless sensor networks (WSNs) are often advertised with high sensing accuracy, long lifetime, and easy deployment. However, they are still not widely used in environmental research due to of poor tool support and high complexity. A wider use of WSNs in field science would enable researchers to address scientific questions that are infeasible today. To address this issue, we designed and prototyped a Software Factory for WSNs that hides the complexity of software development for embedded systems. It exposes a visual domain-specific modeling language and supports code generation for resource constrained devices. The proposed Software Factory simplifies the integration of domain experts into the development process, making WSNs more attractive as a tool for researchers from outside of the computer science field. This could lead to a wider adoption of WSNs in field sciences.
Terrain Classification from UAV Flights Using Monocular Vision

With the popularization of small Unmanned Aerial Vehicles (UAVs) and their usage diversification among various fields, such as aerial mapping applications, it is important to develop better terrain following techniques that rely solely on the vehicle's sensing capabilities. The objective of this paper is to evaluate whether is possible to gather information about terrain inclination and elevation from monocular video captured from such aircrafts. Our approach is biologically inspired by trying to reproduce some insects behaviour with the use of optical flow to infer about the terrain. We built an UAV specifically for this research which uses a gimbal stabilized down-facing camera and flew it at a fixed Above Sea Level (ASL) altitude. After performing preliminary analysis on sparse optical flow data and validating the concept, we moved towards a dense optical flow algorithm and created different descriptors to feed multiple decision trees in order to infer about terrain characteristics. We achieved accuracies of 77.34%, 86.75% and 91.85% depending on the evaluated characteristic, showing that our approach is valid.
Development of a Multiplexed Microfluidic Platform for the Automated Cultivation of Embryonic Stem Cells

We present a multiplexed platform for a microfabricated stem cell culture device. The modular platform contains all the components to control stem cell culture conditions in an automated fashion. It does not require an incubator during perfusion culture and can be mounted on the stage of an inverted fluorescence microscope for high-frequency imaging of stem cell cultures. A pressure-driven pump provides control over the medium flow rate and offers switching of the flow rates. Flow rates of the pump are characterized for different pressure settings, and a linear correlation between the applied pressure and the flow rate in the cell culture devices is shown. In addition, the pump operates with two culture medium reservoirs, thus enabling the switching of the culture medium on-the-fly during a cell culture experiment. Also, with our platform, the culture medium reservoirs are cooled to prevent medium degradation during long-term experiments. Media temperature is then adjusted to a higher controlled temperature before entering the microfabricated cell culture device. Furthermore, the temperature is regulated in the microfabricated culture devices themselves. Preliminary culture experiments are demonstrated using mouse embryonic stem cells.
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy

Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of projections is exponential in the number of dimensions, efficiency is crucial. Moreover, the resulting subspace clusters are often highly redundant, i.e. many clusters are detected multiply in several projections. We propose a novel index for efficient subspace clustering in a novel depth-first processing with in-process-removal of redundant clusters for better pruning. Thorough experiments on real and synthetic data show that INSCY yields substantial efficiency and quality improvements.
Anodal Transcranial Direct Current Stimulation of the Prefrontal Cortex Enhances Complex Verbal Associative Thought

The remote associates test (RAT) is a complex verbal task with associations to both creative thought and general intelligence. RAT problems require not only lateral associations and the internal production of many words but a convergent focus on a single answer. Complex problem-solving of this sort may thus require both substantial verbal processing and strong executive function capacities. Previous studies have provided evidence that verbal task performance can be enhanced by noninvasive transcranial direct current stimulation (tDCS). tDCS modulates excitability of neural tissue depending on the polarity of the current. The after-effects of this modulation may have effects on task performance if the task examined draws on the modulated region. Studies of verbal cognition have focused largely on the left dorsolateral prefrontal cortex (F3 of the 10–20 EEG system) as a region of interest. We planned to assess whether modulating excitability at F3 could affect complex verbal abilities. In Experiment 1 (anodal, cathodal, or sham stimulation over F3 with the reference electrode over the contralateral supraorbital region), we found a significant overall effect of stimulation condition on RAT performance. Post hoc tests showed an increase in performance after anodal stimulation (1 mA) compared to sham (p = .025) and to cathodal stimulation (p = .038). In Experiment 2 (either anodal stimulation at F3 or separately at its homologue F4), we replicated the anodal effect of the first study, but also showed that anodal stimulation of F4 had no effect on RAT performance. These data provide evidence that anodal stimulation of the left dorsolateral prefrontal cortex can improve performance on a complex verbal problem-solving task believed to require significant executive function capacity.
MILOU : a Monte-Carlo for Deeply Virtual Compton Scattering

In this note, we present a new generator for Deeply Virtual Compton Scattering processes. This generator is based on formalism of Generalized Partons Distributions evolved at Next Leading Order (NLO). In the following we give a brief description of this formalism and we explain the main features of the generator for the elastic reactions, as well as for proton dissociation.