Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting Insulin and Insulin-Like Growth Factor Pathways in Epithelial Ovarian Cancer

Ovarian cancer is the most lethal of all gynecological malignancies, due in part to the diagnosis at an advanced stage caused by the lack of specific signs and symptoms and the absence of reliable tests for screening and early detection. Most patients will respond initially to treatment but about 70% of them will suffer a recurrence. Therefore, new therapeutic modalities are urgently needed to overcome chemoresistance observed in ovarian cancer patients. Evidence accumulates suggesting that the insulin/insulin growth factor (IGF) pathways could act as a good therapeutic target in several cancers, including ovarian cancer. In this paper, we will focus on the role of insulin/IGF in ovarian cancer tumorigenesis and treatment.
Privacy-by-Decoy: Protecting location privacy against collusion and deanonymization in vehicular location based services

Wireless networks which would connect vehicles via the Internet to a location based service, LBS, also would expose vehicles to online surveillance. In circumstances when spatial cloaking is not effective, such as when continuous precise location is required, LBSs may be designed so that users relay dummy queries through other vehicles to camouflage true locations. This paper introduces PARROTS, Position Altered Requests Relayed Over Time and Space, a privacy protocol which protects LBS users' location information from LBS administrators even (1) when the LBS requires continuous precise location data in a vehicular ad hoc network, (2) when LBS administrators collude with administrators of vehicular wireless access points (a.k.a. roadside units, or RSUs), and (3) when precise location data might be deanonymized using map databases linking vehicle positions with vehicle owners' home/work addresses and geographic coordinates. Defense against deanonymization requires concealment of endpoints, the effectiveness of which depends on the density of LBS users and the endpoint protection zone size. Simulations using realistic vehicle traffic mobility models varying endpoint protection zone sizes measure improvements in privacy protection.
A hybrid method for parameter estimation of software reliability growth model using Modified Genetic Swarm Optimization with the aid of logistic exponential testing effort function

The modern world is highly engaged with software systems. Determining the quality of a software system is a challenging task. Software reliability plays a vital role in the quality estimation of a software system and highly helped in achieving high-quality software. This paper proposes a hybrid method for estimating the parameters of Software Reliability Growth Model (SRGM) using Modified Genetic Swarm Optimization (MGSO) with the combination of logistic-exponential Testing Effort Function (TEF). Parameter estimation of SRGM is highly helped in reliability prediction which helps in determination of Software quality. Once the parameters are estimated, the parameters are optimized using MGSO. The proposed method implemented on working platform of JAVA and the effectiveness of proposed method estimated by comparing with existing methods.
LinedCut: Image segmentation using single line interaction

In this paper, we propose LinedCut: a novel method for interactive image segmentation which requires only a single line drawing to identify the object of interest in the image. The handy interaction mode can address the problem of object scale very well. Our approach consists of the following three steps: first, a given image is over-segmented into superpixels using superpixel algorithm; secondly, a merging technique based on bag-of-color feature and k-means is applied to merge similar adjacent superpixel pairs; finally, a graph-cut based approach which exploits user interaction information is introduced to get the final segmentation result. Despite its simplicity, we show that the LinedCut method is able to achieve a performance comparable to the state-of-the-art. The method can be easily developed by replacing any one method among the three steps with other methods.
Implementing Optimal Monetary Policy in New-Keynesian Models with Inertia

We consider optimal monetary policy in New Keynesian models with inertia due to lagged effects of inflation and output. We characterize the conditions for the unconditionally optimal equilibrium and compare them with those identifying optimality from the timeless perspective. Implementation of optimal policy is considered via construction of suitable interest-rate rules. We characterize the collection of all interest-rate rules consistent with the optimal equilibrium, and we identify among them an expectations-based rule that guarantees uniqueness and stability under learning.
Multi Agent and Holonic Manufacturing Control

The manufacturing industry will continue to be in the future one of the main wealth generators of the world economy (CMV, 1998). In the last decades world has moved towards a global economy, with markets demanding for products with high quality at lower costs, highly customized and with short life cycles, imposing new requirements on manufacturing enterprises, namely in terms of quality, response, agility and flexibility, that are crucial for an enterprise staying in the business. The traditional manufacturing control systems are not designed to exhibit these capabilities of responsiveness, flexibility, robustness and re-configurability, since they are built upon centralized and hierarchical control structures. They present good production optimization, but a weak response to adopt due to the rigidity and centralization of their control structures. Such centralized hierarchical organization normally leads to situations where the whole system is shutting down by single failures at one point of the system hierarchy (Colombo et al., 2006). The current challenge is to develop collaborative and reconfigurable manufacturing control systems that support efficiently small batches, product diversity, high quality and low costs, by introducing innovative characteristics of adaptation, agility and modularization. Information and communication technologies, and artificial intelligence techniques, have been used for more than two decades addressing this challenge. Namely, agent-based and holonic manufacturing control seem to be suitable to face these requirements such as modularity, scalability, autonomy and re-usability, since they present decentralization of control over distributed structures. When properly designed and implemented, agent-based control systems result in a performance that is flexible, robust, adaptive and fully tolerant, which are key factors for manufacturing success in the increasingly global marketplace. In this chapter, we review the different manufacturing control architecture including centralized and distributed. We discuss about intelligent and distributed manufacturing control systems using emerging paradigms, such as multi-agent systems and holonic manufacturing systems (HMSs), and present two case studies about the applications of agent-based manufacturing control systems for process planning and scheduling. The objective of this chapter is to provide an overview about the application of multi-agent systems and holonic manufacturing principles to manufacturing environment, but to focus on the manufacturing control applications. The chapter is organized as follows. Section 2 reviews the concepts associated with manufacturing control systems, describing the traditional approaches and the distributed 5
A Novel Stereo based Obstacle Avoidance System for Unmanned Aerial Vehicles

The use of autonomous unmanned aerial vehicle (UAV) has been on the rise. They are used to replace an ever-increasing amount of labor. There is a need for unmanned aerial systems to operate safely in the environment around them. The work in this paper aims at creating an obstacle avoidance system using Stereo Vision. The work uses standard block matching algorithms. OpenCV and the KTTI Vision Benchmark suite is used. The ArduPilot SITL simulator is used for running the algorithms and displaying the results. The droneapi is an application that is used to access the UAV’s information and describe new kinds of flight behavior. The application created is known as STOBA (Stereo Based Obstacle Avoidance), which was created to run within the ArduPilot SITL, in order to provide the mentioned obstacle avoidance capability
Multiple kernel collaborative representation based classification

At present, collaborative representation based classification (CRC) is widely used in many pattern classification and recognition tasks. Meanwhile, spatial pyramid matching (SPM) method, which considers the spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we combine CRC and multiple kernel learning approach, propose the multiple kernel collaborative representation based classification (MKCRC) method, and apply it to image classification to learn the weights of different subregions in representing the image. Experimental results have obvious advantages than state-of-the-art methods in several benchmark datasets, such as Caltech101, 102flowers, Scene 15 and UIUC sports.
Approximate Tree Kernels

Convolution kernels for trees provide simple means for learning with tree-structured data. The computation time of tree kernels is quadratic in the size of the trees, since all pairs of nodes need to be compared. Thus, large parse trees, obtained from HTML documents or structured network data, render convolution kernels inapplicable. In this article, we propose an effective approximation technique for parse tree kernels. The approximate tree kernels (ATKs) limit kernel computation to a sparse subset of relevant subtrees and discard redundant structures, such that training and testing of kernel-based learning methods are significantly accelerated. We devise linear programming approaches for identifying such subsets for supervised and unsupervised learning tasks, respectively. Empirically, the approximate tree kernels attain run-time improvements up to three orders of magnitude while preserving the predictive accuracy of regular tree kernels. For unsupervised tasks, the approximate tree kernels even lead to more accurate predictions by identifying relevant dimensions in feature space.
A feasible work-piece placement method for contact-type operations

A method to find a feasible work-piece location for a given desired task-space trajectory is presented in this paper. By making use of the operational space control framework [1], this paper proposes a method to assist the search of the work-piece location with respect to the robot in such a way that the path reachability is always guaranteed. The method proposed in this work does not require the solutions of the inverse kinematics problem, thus, this method is expected to have more flexibility and generality in practice. In addition, since the internal robot posture can be controlled through the null-space controller, optimization of the work-piece placement could be achieved by imposing artificial constrains at the robot joints/links. Simulation is provided to show the feasibility of the proposed algorithm.
Efficient and effective packing and analytical placement for large-scale heterogeneous FPGAs

As FPGA architecture evolves, complex heterogenous blocks, such as RAMs and DSPs, are widely used to effectively implement various circuit applications. These complex blocks often consist of datapath-intensive circuits, which are not adequately addressed in existing packing and placement algorithms. Besides, scalability has become a first-order metric for modern FPGA design, mainly due to the dramatically increasing design complexity. This paper presents efficient and effective packing and analytical placement algorithms for large-scale heterogeneous FPGAs to deal with issues on heterogeneity, datapath regularity, and scalability. Compared to the well-known academic tool VPR, experimental results show that our packing and placement algorithms achieve respective 199.80X and 3.07X speedups with better wirelength, and our overall flow achieves 50% shorter wirelength, with an 18.30X overall speedup.
Efficient Executable File Similarity Detection Scheme by Exploiting Executable Fingerprint

These days, Windows Operating System is widely used and it is dominant than other commercial operating systems. Besides, most of the commercial software vendors provide a number of software mainly for Windows OS. Therefore, software similarity analysis systems for Windows OS are highly useful for software plagiarism detection and copyright protection. However, mostly the software similarity analysis systems and techniques work on source codes, not executables, even though the majority of commercial software barely makes public its source codes. In this paper, we propose a novel scheme for software plagiarism detection for Windows executables. Our technique exploits both extended basic blocks' opcode sequences and imported API functions information in executable files. We implemented our scheme and measured its performance through several kinds of experiments using code samples and different versions of commercial software.
A comparative study between motivated learning and reinforcement learning

This paper analyzes advanced reinforcement learning techniques and compares some of them to motivated learning. Motivated learning is briefly discussed indicating its relation to reinforcement learning. A black box scenario for comparative analysis of learning efficiency in autonomous agents is developed and described. This is used to analyze selected algorithms. Reported results demonstrate that in the selected category of problems, motivated learning outperformed all reinforcement learning algorithms we compared with.
Rendering viscoelasticity with Series Elastic Actuators using cascade control

Rotary Series Elastic Actuators (SEAs) are largely adopted to safely and accurately modulate interaction torques when robots operate in close contact with humans. Torque control is often based on a cascade scheme including PI regulators for a velocity controller nested in a torque controller. This solution is simple, robust and can potentially guarantee coupled stability. A high-level impedance control loop is also commonly added to regulate the behavior of the interaction port as a desired virtual viscoelastic body. In the present work, passivity is analyzed when a cascade controlled SEA is employed to display a virtual parallel spring-damper system. The case of a null desired impedance and of a pure spring are also tackled. The range of renderable mechanical impedance and guidelines for the selection of the control gains are derived.
Deformation monitoring in Kos Island Hellenic volcanic arc, Eastern Greece) using differential interferometry

In this study SAR interferometry technique has been applied over Kos Island (Hellenic Volcanic Arc) in order to detected pre-, coand post-seismic deformation and to compare with the deformation detected in Nisyros Island for the same period using the same interferometric pairs. The seismic unrest activity observed in Nisyros volcano (1996-1999) could produce crustal deformation in the adjacent island of Kos. The results of the interferometric processing show that coand post-seismic deformation was not possible to be detected due to the obtained low coherence. Contrary the results of the interferometric pair of the pre-seismic unrest period show two fringes of deformation in the southwest part of the island, the same amount of deformation was observed in Nisyros island.
Mixed effects models in neurolinguistics: is there a way beyond univariate analysis?

Mixed effects models have recently gained some attention in psychoand neurolinguistics applied research, eve n though their adoption is still not extensive enough (Meyers and Beretvas, 2006). In particular, psychoand neurolinguistics data have a specific structure where subjects and items are reciprocally nested. Each subject i s crossed within each item, since every person is studied at answering each item, and all items are asked to a ll subjects. The evaluation of an answer is typically binary (correct or incorrect), while the time needed for an swering, namely latency or reaction time (RT), is traditionally processed by prior averaging. These data are th en analysed by means of Anova models, once again for items and subjects separately. Several objections have been raised to this approach, and the nested reciprocal relation between items and su bjects has been correctly recognised as a completely cross-classified structure (Quen and van den Bergh, 200 6; Baayen, 2008). This frame of multilevel mixed modeling with crossed random effects for items and subjects has so far shown to match psycholinguistic and neurolinguistic experiments and results have produced sound contributions, for instance in the theme of compound words processing and representation (Crippa & Marelli, 2009). Further applications have endorsed this approach and hint a possible extension from univariate to biva Int. Statistical Inst. : Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS008) p.6074
Automatic performance optimization in ViennaCL for GPUs

Highly parallel computing architectures such as graphics processing units (GPUs) pose several new challenges for scientific computing, which have been absent on single core CPUs. However, a transition from existing serial code to parallel code for GPUs often requires a considerable amount of effort. The Vienna Computing Library (ViennaCL) presented in the beginning of this work is based on OpenCL to support a wide range of hardware and aims at providing a high-level C++ interface that is mostly compatible with the existing CPU linear algebra library uBLAS shipped with the Boost libraries. As a general purpose linear algebra library, ViennaCL runs on a variety of GPU boards from different vendors pursuing different hardware architectures. As a consequence, the optimal number of threads working on a problem in parallel depends on the available hardware and the algorithm executed thereon. We present an optimization framework, which extracts suitable thread numbers and allows ViennaCL to automatically optimize itself to the underlying hardware. The performance enhancement of individually tuned kernels over default parameter choices range up to 25 percent for the kernels considered on high-end hardware, and up to a factor of seven on low-end hardware.
Evaluation of approaches to solving electrocardiographic imaging problem

Electrocardiographic imaging (ECGI) is a widely used method of computing potentials on the epicardium from measured or simulated potentials on the torso surface. The main challenge of the electrocardiographic imaging problem lies in its intrinsic ill-posedness, and many regularization techniques have been developed to smooth out the solution. It is still an ongoing research subject to choose proper regularization methods and to determine their proper amount for obtaining clinically acceptable solutions. This study systematically compares various regularization techniques for the ECGI problem under a unified simulation framework. The framework consists of an electrolytic human torso tank containing a live canine heart, with the cardiac source being modeled by potentials measured on a cylindrical cage placed around the heart. We tested 14 different regularization techniques to solve the inverse problem of recovering epicardial potentials, and found that non-quadratic methods (total variation algorithms) were the most robust and resulted in the lowest reconstruction errors.
Comparative Analysis of Zinc Finger Proteins Involved in Plant Disease Resistance

A meta-analysis was performed to understand the role of zinc finger domains in proteins of resistance (R) genes cloned from different crops. We analyzed protein sequences of seventy R genes of various crops in which twenty six proteins were found to have zinc finger domains along with nucleotide binding sites - leucine rice repeats (NBS-LRR) domains. We identified thirty four zinc finger domains in the R proteins of nine crops and were grouped into 19 types of zinc fingers. The size of individual zinc finger domain within the R genes varied from 11 to 84 amino acids, whereas the size of proteins containing these domains varied from 263 to 1305 amino acids. The biophysical analysis revealed that molecular weight of Pi54 zinc finger was lowest whereas the highest one was found in rice Pib zinc finger named as Transposes Transcription Factor (TTF). The instability (R2 = 0.95) and the aliphatic (R2 = 0.94) indices profile of zinc finger domains follows the polynomial distribution pattern. The pairwise identity analysis showed that the Lin11, Isl-1 & Mec-3 (LIM) zinc finger domain of rice blast resistance protein pi21 have 12.3% similarity with the nuclear transcription factor, X-box binding-like 1 (NFX) type zinc finger domain of Pi54 protein. For the first time, we reported that Pi54 (Pi-kh-Tetep), a rice blast resistance (R) protein have a small zinc finger domain of NFX type located on the C-terminal in between NBS and LRR domains of the R-protein. Compositional analysis depicted by the helical wheel diagram revealed the presence of a hydrophobic region within this domain which might help in exposing the LRR region for a possible R-Avr interaction. This domain is unique among all other cloned plant disease resistance genes and might play an important role in broad-spectrum nature of rice blast resistance gene Pi54.
Academic procrastinators and perfectionistic tendencies among graduate students.

Research has documented that most college students delay on academic tasks. Surprisingly, graduate students may procrastinate on academic tasks even more than do undergraduate students. Perfectionism also has been found to be high among graduate students. It is likely that for graduate students, delaying academic tasks such as writing a term paper is indicative of perfectionism. Thus, this study investigated the relationship between academic procrastination and perfectionism among 135 graduate students, who were administered the Procrastination Assessment Scale-Students and the Multidimensional Perfectionism Scale. A canonical correlation analysis revealed that fear of failure, a component of academic procrastination was related to self-oriented perfectionism and socially prescribed perfectionism. Other-oriented perfectionism, the third dimension of perfectionism, acted as a suppressor variable. Implications are discussed.
Optimal Identification and Estimation in Practical Noisy Environments

The practical environment settings are always dynamic and unpredictable. And the adaptive solution is deteriorated seriously unless we can accurately determine when the filter's adaptation actually converges. In this paper, based on the principle of orthogonality, a scheme is proposed for sharply judging the iteration's convergence to obtain the optimal estimation in practically unknown stationary or nonstationary circumstances. The discriminant obtained through the estimated mean-square values of the desired, output and error signals at each iteration cycle, can be updated according to the varying characteristics of the actual input signal. Cases of both computer simulations and real applications are studied to validate its effectiveness in stationary and nonstationary environments