Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Hybrid Review Process (EHRP)

Software review is an effective and best practice to verify the software quality. This paper presents an effective and efficient review process. It overcomes the obstacles facing the current review processes. Current review processes face these obstacles due to their rigid and time consuming nature. Proposed review process introduces twofold review activity, an effectual meeting procedure and course of actions for future improvements. It is an attempt to minimize the inflexible requirements of the review meetings. It aimed at reduction of synchronous review meeting by removing less critical defects before synchronous review meeting. It is evaluated through an experiment and comparative analysis, which has provided effective results.
Comparison of faster R-CNN models for object detection

Object detection is one of the important problems for autonomous robots. Faster R-CNN, one of the state-of-the-art object detection methods, approaches real time application; nevertheless, computational time lies borderline of real time application, i.e. 5fps with VGG16 model in K40 GPU system in [1]. Moreover, computation time depends on model and image crop size, but precision is also affected; usually, time and precision have trade-off relation. By adjusting input image size in spite of downgrading performance, computation time meets criteria for one model. Therefore, selection of a model is one of the important problems when faster R-CNN based object detection system for an autonomous robot is constructed. In this paper, we convert several state-of-the-art models from convolution neural network (CNN) for image classification. Then, we compare converted models with several image crop size in terms of computation time and detection precision. We will utilize those comparison data for selecting a proper detection model in case a robot needs to perform an object detection task.
Low-latency Memory-Mapped I/O for Data-Intensive Applications on Fast Storage Devices

Thesedays, along with read()/write(), mmap() is used to file I/O in data-intensive applications as an alternative method of I/O on emerging low-latency device such as flash-based SSD. Although utilizing memory-mapped file I/O have many advantages, it does not produce much benefit when combined with large-scale data and fast storage devices. When the working set of an application accessing file with mmap() is larger than the size of physical memory, the I/O performance is severely degraded compared to the application with read/write(). This is mainly due to the virtual memory subsystem that does not reflect the performance feature of the underlying storage device. In this paper, we examined linux virtual memory subsystem and mmap() I/O path to figure out the influence of low-latency storage devices on the existing virtual memory subsystem. Also, we suggest some optimization policies to reduce the overheads of mmap() I/O and implement the prototype in a recent Linux kernel. Our solution guarantees that 1) memory-mapped I/O will be several times faster than read-write I/O when cache-hit ratio becomes high, and 2) the former will show at least the performance of the latter even when cache-miss frequently occurs and the overhead of mapping/unmapping pages becomes significant, which are not achievable by the existing virtual memory subsystem.
Caucasian and Asian Specific Rheumatoid Arthritis Risk Loci Reveal Limited Replication and Apparent Allelic Heterogeneity in North Indians

Genome-wide association studies and meta-analysis indicate that several genes/loci are consistently associated with rheumatoid arthritis (RA) in European and Asian populations. To evaluate the transferability status of these findings to an ethnically diverse north Indian population, we performed a replication analysis. We investigated the association of 47 single-nucleotide polymorphisms (SNPs) at 43 of these genes/loci with RA in a north Indian cohort comprising 983 RA cases and 1007 age and gender matched controls. Genotyping was done using Infinium human 660w-quad. Association analysis by chi-square test implemented in plink was carried out in two steps. Firstly, association of the index or surrogate SNP (r2>0.8, calculated from reference GIH Hap-Map population) was tested. In the second step, evidence for allelic/locus heterogeneity at aforementioned genes/loci was assessed for by testing additional flanking SNPs in linkage equilibrium with index/surrogate marker. Of the 44 European specific index SNPs, neither index nor surrogate SNPs were present for nine SNPs in the genotyping array. Of the remaining 35, associations were replicated at seven genes namely PTPN22 (rs1217407, p = 3×10−3); IL2–21 (rs13119723, p = 0.008); HLA-DRB1 (rs660895, p = 2.56×10−5; rs6457617, p = 1.6×10−09; rs13192471, p = 6.7×10−16); TNFA1P3 (rs9321637, p = 0.03); CCL21 (rs13293020, p = 0.01); IL2RA (rs2104286, p = 1.9×10−4) and ZEB1 (rs2793108, p = 0.006). Of the three Asian specific loci tested, rs2977227 in PADI4 showed modest association (p<0.02). Further, of the 140 SNPs (in LE with index/surrogate variant) tested, association was observed at 11 additional genes: PTPRC, AFF3, CD28, CTLA4, PXK, ANKRD55, TAGAP, CCR6, BLK, CD40 and IL2RB. This study indicates limited replication of European and Asian index SNPs and apparent allelic heterogeneity in RA etiology among north Indians warranting independent GWAS in this population. However, replicated associations of HLA-DRB1, PTPN22 (which confer ∼50% of the heritable risk to RA) and IL2RA suggest that cross-ethnicity fine mapping of such loci is apposite for identification of causal variants.
Structural information processing in early vision using Hebbian-based mean shift

This paper presents a biologically plausible model for the structural information processing in early vision. Our investigation on the frequency spectrum of natural images filtered by the retina shows that the DC component containing much redundancy information and the high frequency components containing much noisy information are reduced, while the middle and low frequency components containing much structural information are enhanced. Simple cells in the primary visual cortex (V1) extract structural primitives from the filtered signals resulting in the emergence of diverse receptive field shapes. We name these structural primitives as structors, and study the neural mechanisms responsible for this diversity of V1 simple cell receptive field shapes. Sparse coding with the L0-norm constraint is reexamined which suggests that the local structure of natural images is determined by few structors regardless of their coefficients. We perform an analysis on the spatial distribution of the input signal and prove that signals in the neighborhood of a special structor has a star shape and peaks at the structor. That is, the structors are the modes of the probability density function of the input signal, and learning the structors can be interpreted as mode detection. Mean sift method is applied to detect modes, and the updating rule for the mean shift appears to be Hebbian. We propose the Hebbian-based mean shift to simulate the emergence of the diversity of simple cell receptive field shapes. The simulation results demonstrate the robustness of the proposed algorithm in producing both Gabor-like and blob-like structors.
Efficient data distribution strategy for join query processing in the cloud

There are many advantages for large scale data management in the cloud. More and more companies start to migrate their data into cloud data management systems. Join query becomes a challenging research problem in cloud. To finish a join query in the cloud, data among different nodes need to be transferred. The arrangement of data transmission and local data processing is known as a distribution strategy for a query. The transmission cost (network workload between servers and the transmission time delay) will be very high if the strategy is not properly chosen. Existing cloud systems either do not support join query or just use MapReduce to support some simple join queries. The problem of using redundant data for join query optimization in cloud environment is studied in this paper. Two novel algorithms, Set Cover based algorithm (SC) and Minimum Element based algorithm (ME), are proposed to reduce data transmission cost. The experiment results demonstrate that the proposed methods can greatly reduce the data transmission cost compared with the naive method. Besides, the result is very close to the optimal strategy.
Portfolio Size Effect in Retirement Accounts: What Does It Imply for Lifecycle Asset Allocation Funds?

Lifecycle funds offered by retirement plan providers allocate aggressively to risky asset classes when the employee participants are young, gradually switching to more conservative asset classes as they grow older and approach retirement. This approach focuses on maximizing growth of the accumulation fund in the initial years and preserving its value in the later years. The authors simulate terminal wealth outcomes based on conventional lifecycle asset allocation rules as well as on contrarian strategies that reverse the direction of asset switching. The evidence suggests that the growth in portfolio size over time significantly impacts the asset allocation decision. Due to the portfolio size effect that is observed by the authors, the terminal value of accumulation in retirement accounts is influenced more by the asset allocation strategy adopted in later years relative to that adopted in early years. By mechanistically switching to conservative assets in the later years of a plan, lifecycle strategies sacrifice significant growth opportunity and prove counterproductive to the participant's wealth accumulation objective. The authors' conclude that this sacrifice does not seem to be compensated adequately in terms of reducing the risk of potentially adverse outcomes.
Inter Genre Similarity Modeling For Automatic Music Genre Classification

Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modeling (IGS) to improve the automatic music genre classification performance. Inter-genre similarity information is extracted over the mis-classified feature population. Once the inter-genre similarity is modeled, elimination of the inter-genre similarity reduces the inter-genre confusion and improves the identification rates. Inter-genre similarity modeling is further improved with iterative IGS modeling and score modeling for IGS elimination. Experimental results with promising classification improvements are provided
Generalized Domains for Empirical Evaluations in Reinforcement Learning

Many empirical results in reinforcement learning are based on a very small set of environments. These results often represent the best algorithm parameters that were found after an ad-hoc tuning or fitting process. We argue that presenting tuned scores from a small set of environments leads to method overfitting, wherein results may not generalize to similar environments. To address this problem, we advocate empirical evaluations using generalized domains: parameterized problem generators that explicitly encode variations in the environment to which the learner should be robust. We argue that evaluating across a set of these generated problems offers a more meaningful evaluation of reinforcement learning algorithms.
Pneumatic assistant of one degree of freedom for lifting

Exoskeletons are wearable robots i.e. robots coupled to the body of a user and commanded by him/her. The fields of application involve among others, the military as well as the medical industries. This paper presents the design of part of an exoskeleton aimed to enhance the performance of people that have had spinal injury or that suffer from some problem or pain in the lumbar area. An experimental platform was built that allows the wearer to perform his/her tasks without making a big effort that could cause pain or injuries in the lumbar zone. The experimental platform employs Pneumatic Artificial Muscles (PAM) as actuators because they are suitable for applications were force is required. However being these actuators highly nonlinear devices, the control as well as the electronics involved in the design of the experimental set up are not a simple tasks. The experimental set up presented here is a one degree of freedom device attached to the trunk of a user. To start with a PD controller was employed. This set up is part of a bigger project that considers a complete exoskeleton that involves the four limbs and the middle part of a human body. However this device can be used to assist, as mentioned before, people with problems in the lumbar area.
Robust blind beamforming algorithm via the oblique projection method

A novel robust least squares constant modulus algorithm (LSCMA) is proposed for blind adaptive beamforming, which is based on explicit modeling of uncertainty in the desired signal array. To improve robustness, the weight vector is optimized to involve minimization of cost function, while imposing the oblique projection constraint on the weight vector and maintaining a distortionless response for the worst-case signal steering vector. The proposed algorithm appears to be an appealing technique for blind adaptive beamforming that combines excellent robustness with low computational complexity. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement.
Actin, microvilli, and the fertilization cone of sea urchin eggs

Sea urchin eggs and oocytes at the germinal vesicle stage were fixed at various times after insemination, and thin sections were examined. Actin filaments can first be found in the cortical cytoplasm 1 min after insemination, and by 2 min enormous numbers of filaments are present. At these early stages, the filaments are only occasionally organized into bundles, but one end of many filaments contacts the plasma membrane. By 3 min, and even more dramatically by 5 min after insemination, the filaments become progressively more often found in bundles that lie parallel to the long axis of the microvilli and the fertilization cones. By 7 min, the bundles of filaments in the cone are maximally pronounced, with virtually all the filaments lying parallel to one another. Decoration of the filaments with subfragment 1 of myosin shows that, in both the microvilli and the cones, the filaments are unidirectionally polarized with the arrowheads pointing towards the cell center. The efflux of H+ from the eggs was measured as a function of time after insemination. The rapid phase of H+ efflux occurs at the same time as actin polymerization. From these results it appears that the formation of bundles of actin filaments in microvilli and in cones is a two-step process, involving actin polymerization to form filaments, randomly oriented but in most cases having one end in contact with the plasma membrane, followed by the zippering together of the filaments by macromolecular bridges.
Drosophila miR-124 regulates neuroblast proliferation through its target anachronism

MicroRNAs (miRNAs) have been implicated as regulators of central nervous system (CNS) development and function. miR-124 is an evolutionarily ancient, CNS-specific miRNA. On the basis of the evolutionary conservation of its expression in the CNS, miR-124 is expected to have an ancient conserved function. Intriguingly, investigation of miR-124 function using antisense-mediated miRNA depletion has produced divergent and in some cases contradictory findings in a variety of model systems. Here we investigated miR-124 function using a targeted knockout mutant and present evidence for a role during central brain neurogenesis in Drosophila melanogaster. miR-124 activity in the larval neuroblast lineage is required to support normal levels of neuronal progenitor proliferation. We identify anachronism (ana), which encodes a secreted inhibitor of neuroblast proliferation, as a functionally important target of miR-124 acting in the neuroblast lineage. ana has previously been thought to be glial specific in its expression and to act from the cortex glia to control the exit of neuroblasts from quiescence into the proliferative phase that generates the neurons of the adult CNS during larval development. We provide evidence that ana is expressed in miR-124-expressing neuroblast lineages and that ana activity must be limited by the action of miR-124 during neuronal progenitor proliferation. We discuss the possibility that the apparent divergence of function of miR-124 in different model systems might reflect functional divergence through target site evolution.
Spectrum sensing in Cognitive Radio based on compressive measurements

Cognitive Radio has attracted a lot of attention in the recent past due to the promise of a better utilization of the available spectrum. However, it faces many constraints in its implementation. Current spectrum sensing techniques are either computationally expensive or are not accurate enough. We propose a compressive signal processing (CSP) based approach for spectrum sensing that provides good accuracy at lower complexity. Since spectrum sensing involves sampling wideband signals, the sampling rates mandated by the Nyquist-Shannon sampling criterion tend be very high. This results in a heavy burden on the hardware devices (ADCs et al) and adds to the computational woes. Compressed sensing seems to be a natural solution to this problem. But typical compressed sensing algorithms involve signal reconstruction, and can be computationally expensive. By noting that spectrum sensing is an inference problem, we adopt the CSP approach that avoids reconstruction. Simulation results demonstrate that the proposed CSP based detector provides high accuracy at a reasonable complexity.
Learning of gestures by imitation A probabilistic approach based on dynamical systems

We present a probabilistic approach to learning robust models of human motion through imitation. The combination of Hidden Markov Model (HMM), Gaussian Mixture Regression (GMR) and dynamical systems allows us to extract redundancies across multiple demonstrations and build timeindependent models to reproduce the dynamics of the observed movements. The approach is first compared with state-of-theart approaches by using generated trajectories sharing similar characteristics to those of humans. Three applications on different types of robots are then presented. An experiment with the iCub humanoid robot acquiring a bimanual dancing motion is first presented to show that the system can cope with cyclic and crossing motions. An experiment with a 7 DOFs WAM robotic arm learning the motion of hitting a ball with a table tennis racket is presented to highlight the possibility to encode several movements in a single model. Finally, an experiment with a HOAP-3 humanoid robot holding a spoon and learning to feed the Robota humanoid robot is presented. It shows the capability of the system to handle several constraints simultaneously.
SDN application segregation, concurrency and order of execution

One of the great attractions of Software Defined Networking (SDN) has been the promise of transforming the network by providing a degree of flexibility and automation that until now has never been available. Of interest however is that all of these exciting solutions are usually discussed in isolation and rarely do proponents allude to the fact that numerous applications can be deployed simultaneously. Unfortunately most SDN Controllers are incapable of managing multiple applications that require access to the same events notifications. In this paper, we introduce the concept of an Event Arbitration Manager (EAM), a modified event handler that provides mediation between applications on a single SDN controller. We show that the introduction of an event mediation layer between SDN applications would reduce conflicts between multiple applications on the same SDN controller and allows them to respond to controller events messages equally.
A framework for the automatic processing of Basque

In this paper we present the tools (lemmatizer-tagger, morphological analyzer, l inguistic environment), applications (spelling checker) and resources (lexical database, corpora and grammars) developed for the treatment of Basque by the IXA Group. Besides, the main research lines and the strategy followed by the group are presented.
Approximation algorithms for co-clustering

Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted severe attention in the past few years. Unfortunately, to date, most of the algorithmic work on this problem has been heuristic in nature. In this work we obtain the first approximation algorithms for the co-clustering problem. Our algorithms are simple and obtain constant-factor approximation solutions to the optimum. We also show that co-clustering is NP-hard, thereby complementing our algorithmic result.
Proof of concept of an RFID-enabled supply chain in a B2B e-commerce environment

Based on a field study conducted in the utility industry, this paper examines the impacts and potential benefits generated by an RFID application in one specific supply chain. Through a detailed investigation of the underlying business processes, we demonstrate how process optimization can be achieved when integrating RFID technology into information systems applications. A proof of concept (POC) is also conducted in laboratory settings to demonstrate the feasibility of the proposed scenarios.
Autoepistemic logic

Autoepistemic logic is one of the principal modes of nonmonotonicreasoning. It unifies several other modes of nonmonotonic reasoning andhas important application in logic programming. In the paper, a theoryof autoepistemic logic is developed. This paper starts with a briefsurvey of some of the previously known results. Then, the nature ofnonmonotonicity is studied by investigating how membership ofautoepistemic statements in autoepistemic theories depends on theunderlying objective theory. A notion similar to set-theoretic forcingis introduced. Expansions of autoepistemic theories are alsoinvestigated. Expansions serve as sets of consequences of anautoepistemic theory and they can also be used to define semantics forlogic programs with negation. Theories that have expansions arecharacterized, and a normal form that allows the description of allexpansions of a theory is introduced. Our results imply algorithms todetermine whether a theory has a unique expansion. Sufficient conditions(stratification) that imply existence of a unique expansion arediscussed. The definition of stratified theories is extended and (undersome additional assumptions) efficient algorithms for testing whether atheory is stratified are proposed. The theorem characterizing expansionsis applied to two classes of theories, K1-theoriesand ae-programs. In each case, simple hypergraph characterization ofexpansions of theories from each of these classes is given. Finally,connections with stable model semantics for logic programs with negationis discussed. In particular, it is proven that the problem of existenceof stable models is NP-complete. —Authors' Abstract