Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Clustering in Wireless Sensor Network : Considering Nodes with Lowest Energy

In recent years, wireless sensor networks (WSNs) have gained increasing attention from researchers and scholars. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two stages, the setup stage and steady stage. The most important point in this algorithm is cluster head selection. Cluster head selection is important because, a good clustering guarantees energy efficiency and load balancing in the network. For load balancing in WSNs, network overload should be removed from the weak nodes (nodes with lower residual energy) and transmitted to the more powerful nodes. In this paper weak nodes decide which node become cluster head. Weak node choice cluster head based on node’s weight. This weight is combined of residual energy and distance. Finally, the simulation results demonstrate that our proposed distributed clustering approach is more effective in prolonging the network life-time compared with LEACH.
An empirical study of the effectiveness of speech-recognition-based pronunciation training

We have tested a fixed version of the Fluency foreign language pronunciation trainer to determine whether users actually improve their pronunciation through its use. The Fluency system uses the CMU SPHINX II speech recognizer to pinpoint pronunciation errors and then gives suggestions as to how to correct them. Users in the experiment we will describe in this paper were to learn to pronounce the voiced and voiceless interdental fricatives / (cid:0) / and / (cid:1) /, as in "thin" and "that. " The choice of these two phones allowed us to attract users from several native languages. In this paper, we present the results of this study, fully describing the test situation and drawing conclusions about Fluency as a testbed.
On the choice of parameters of the cost function in nested modular RNN's

We address the choice of the coefficients in the cost function of a modular nested recurrent neural-network (RNN) architecture, known as the pipelined recurrent neural network (PRNN). Such a network can cope with the problem of vanishing gradient, experienced in prediction with RNN's. Constraints on the coefficients of the cost function, in the form of a vector norm, are considered. Unlike the previous cost function for the PRNN, which included a forgetting factor motivated by the recursive least squares (RLS) strategy, the proposed forms of cost function provide "forgetting" of the outputs of adjacent modules based upon the network architecture. Such an approach takes into account the number of modules in the PRNN, through the unit norm constraint on the coefficients of the cost function of the PRNN. This is shown to be particularly suitable, since due to inherent nesting in the PRNN, every module gives its full contribution to the learning process, whereas the unit norm constrained cost function introduces a sense of forgetting in the memory management of the PRNN. The PRNN based upon a modified cost function outperforms existing PRNN schemes in the time series prediction simulations presented.
The p-Center Problem with Connectivity Constraint 1

Let G(V, E, W) be a graph with n-vertex-set V and m-edge-set E in which each edge e is associated with a positive distance W(e). The p-Center problem is to locate some kind of facilities at p vertices of G to minimize the maximum distance between any vertex and the nearest facility corresponding to that vertex. This paper proposes an additional practical constraint. We restrict that the p vertices where the facilities are located must be connected, i.e., the subgraph induced by the p facility vertices must be connected. The resulting problem is called the Connected p-Center problem (the CpC problem). We first show that the CpC problem is NP-Hard on bipartite graphs and split graphs. Then, an O(n)-time algorithm for the problem on trees is proposed. Finally, we extend this algorithm to trees with forbidden vertices, i.e. some vertices in V cannot be selected as center vertices, and the time-complexity is also O(n).
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings

Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Our model treats training data as noisy and uses non-parametric variant of hinge loss function. Experiments show that the proposed model outperforms previous state-of-the-art methods on two publicly available datasets, namely FIGER (GOLD) and BBN with an average relative improvement of 2.69% in micro-F1 score. Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems. These approaches of transferring knowledge further improve the performance of respective models.
SPOTT: A Mobile Robot Control Architecture for Unknown or Partially Known Environments

A robot control architecture called SPOTT 1 is proposed and implemented as a real-time and parallel system of concurrently executing and cooperati ng modules. SPOTT provides a bridge for linkin? ; b ehaviora l (i.e., reactive) and symbolic control. T he control system is a realt ime A I system which is responsible for dynamically adapting to changing environmental circumstances in order to successfully execute and completr a set of navigational tasks for an autonomous mobile robot . SPOTT consists of a behavioral controller , a local dynamic path planner, and a global path planner , as well as a map and a graphical user interface. T he behavioral control formalism is called TR+ a nd is based on an adaptation and f'xtension of the Teleo-Reac tive (TR) formali sm introduced by Nils Nilsson. TR+ rules make decisions which affect actuator control and map database maintenance. :\ dynami c local path planner continually polls the map database in order to navigate around newly encountered ubstaclr~ . The local dynamic path planner i~ based on cl. potential field method using harmonic functions, which are guaranteed to have no spurious local minima. The global planning module advises the local planning module of t he effects of a global goal. We have developed a real-lime parallel implementation of SPOTT using a me~­ sage passing softwa.re package called PVM. Navigat ional experiments have consisted of moving the robot in an office and laboratory environment to known and unknown spatial locations with no or a parti al a priori map.
Implications of Survey Sampling Design for Missing Data Imputation

Previous studies that analyzed multiple imputation using survey data did not take into account the survey sampling design. The objective of the current study is to analyze the impact of survey sampling design missing data imputation, using multivariate multiple imputation method. The results of the current study show that multiple imputation methods result in lower standard errors for regression analysis than the regression using only complete observation. Furthermore, the standard errors for all regression coefficients are found to be higher for multiple imputation with taking into account the survey sampling design than without taking into account the survey sampling design. Hence, sampling based estimation leads to more realistic standard errors.
Prioritized multi-task motion control of redundant robots under hard joint constraints

We present an efficient method for motion control of redundant robots performing multiple prioritized tasks in the presence of hard bounds on joint range, velocity, and acceleration/ torque. This is an extension of our recently proposed SNS (Saturation in the Null Space) algorithm developed for single tasks. The method is defined at the level of acceleration commands and proceeds by successively discarding one at a time the commands that would exceed their bounds for a task of given priority, and reintroducing them at their saturated levels by projection in the null space of a suitable Jacobian associated to the already considered tasks. When processing all tasks in their priority order, a correct preemptive strategy is realized in this way, i.e., a task of higher priority uses in the best way the feasible robot capabilities it needs, while lower priority tasks are accommodated with the residual capability and do not interfere with the execution of higher priority tasks. The algorithm automatically integrates a multi-task least possible scaling strategy, when some ordered set of original tasks is found to be unfeasible. Simulation and experimental results on a 7-dof lightweight KUKA LWR IV robot illustrate the good performance of the method.
Effects of large-scale habitat variability on condition of demersal exploited fish in the north-western Mediterranean

We examine the variation in condition of ten exploited fish species as an indicator of large-scale habitat quality in the north-western Mediterranean. To determine fish condition, we used morphometric (Fulton's K) and physiological (hepatosomatic, HSI; digestivesomatic, DSI; and gonadosomatic, GSI) indices. Generalized linear models (GLMs) were applied to assess the influence of depth, latitude, length, year, sex and reproductive status on condition. The GLMs incorporating all independent variables accounted for between 25 and 74% of the variance in Fulton's K condition factor, 15-77% of the variance in HSI, 14-35% of the variance in DSI, and 77% of the variance in GSI. The mean condition factor of the species studied showed considerable interannual fluctuations along with an increasing trend over the time series (1994-2001). Length is an important explanatory variable in explaining condition of fish species. Condition typically increased with fish length and explained 72% of the deviance in GSI of M. barbatus. For some species, there were differences in condition due to sex and reproductive status, with adults being usually better in condition than juveniles and, among adults, females better than males. The influence of latitude and depth on condition of a given species varied according to the index used to assess condition. When there was a significant effect, fish with the highest condition factor, HSI and/or DSI inhabited the shallowest and northernmost parts of the area of study. Considering that condition influences growth, reproduction and survival of fish, our results support the importance of shallow habitats to productivity of demersal stocks in the Mediterranean Sea.
Receptor Sorting within Endosomal Trafficking Pathway Is Facilitated by Dynamic Actin Filaments

Early endosomes (EEs) are known to be a sorting station for internalized molecules destined for degradation, recycling, or other intracellular organelles. Segregation is an essential step in such sorting, but the molecular mechanism of this process remains to be elucidated. Here, we show that actin is required for efficient recycling and endosomal maturation by producing a motile force. Perturbation of actin dynamics by drugs induced a few enlarged EEs containing several degradative vacuoles and also interfered with their transporting ability. Actin repolymerization induced by washout of the drug caused the vacuoles to dissociate and individually translocate toward the perinuclear region. We further elucidated that cortactin, an actin-nucleating factor, was required for transporting contents from within EEs. Actin filaments regulated by cortactin may provide a motile force for efficient sorting within early endosomes. These data suggest that actin filaments coordinate with microtubules to mediate segregation in EEs.
Generalizing Agile Software Development Life Cycle

In last decade, various agile methods have been introduced and used by software industry. It has been observed that many practitioners are using hybrid of agile methods and traditional methods. The knowledge of agile software development process about the theoretical grounds, applicability in large development settings and connections to establish software engineering disciplines remain mostly in dark. It has been reported that it is difficult for average manager to implement agile method in the organization. Further, every agile method has its own development cycle that brings technological, managerial and environmental changes in organization. A proper roadmap of agile software development in the form of agile software development life cycle can be developed to address the aforesaid issues of agile software development process. Thus, there is strong need of agile software development life cycle that clearly defines the phases included in any agile method and also describes the artifacts of each phase. This generalization of agile software development life cycle provides the guideline for average developers about usability, suitability, applicability of agile methods.
Advances in alphadigit recognition using syllables

We present a set of experiments which explore the use of syllables for recognition of continuous alphadigit utterances. In this system, syllables are used as the primary unit of recognition. This work was motivated by our need to verify and isolate phenomena seen when performing syllable-based experiments on the Switchboard corpus. The performance of our base syllable system is better than a crossword triphone system while requiring a small portion of the resources necessary for triphone systems. All experiments were performed on the OGI Alphadigits corpus, which consists of telephone-bandwidth alphadigit strings. The word error rate (WER) of the best syllable system (context-independent syllables) reported here is 11.1% compared to 12.2% for a crossword triphone system.
Validation of a radiosonde‐based cloud layer detection method against a ground‐based remote sensing method at multiple ARM sites

Cloud vertical structure is a key quantity in meteorological and climate studies, but it is also among the most difficult quantities to observe. In this study, we develop a long‐term (10 years) radiosonde‐based cloud profile product for the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP), Tropical Western Pacific (TWP), and North Slope of Alaska (NSA) sites and a shorter‐term product for the ARM Mobile Facility (AMF) deployed in Shouxian, Anhui Province, China (AMF‐China). The AMF‐China site was in operation from 14 May to 28 December 2008; the ARM sites have been collecting data for over 15 years. The Active Remote Sensing of Cloud (ARSCL) value‐added product (VAP), which combines data from the 95‐GHz W‐band ARM Cloud Radar (WACR) and/or the 35‐GHz Millimeter Microwave Cloud Radar (MMCR), is used in this study to validate the radiosonde‐based cloud layer retrieval method. The performance of the radiosonde‐based cloud layer retrieval method applied to data from different climate regimes is evaluated. Overall, cloud layers derived from the ARSCL VAP and radiosonde data agree very well at the SGP and AMF‐China sites. At the TWP and NSA sites, the radiosonde tends to detect more cloud layers in the upper troposphere.
Using a Flexible Fault-Tolerant Cache ( FFT-Cache ) to Improve Reliability in Ultra Low Voltage Operation

Caches are known to consume a large part of total microprocessor power. Traditionally, voltage scaling has been used to reduce both dynamic and leakage power in caches. However, aggressive voltage reduction causes process-variation-induced failures in cache SRAM arrays, which compromise cache reliability. In this paper, we propose Flexible Fault-Tolerant Cache (FFT-Cache) that uses a flexible defect map to configure its architecture to achieve significant reduction in energy consumption through aggressive voltage scaling, while maintaining high error reliability. FFT-Cache uses a portion of faulty cache blocks as redundancy – using block-level or line-level replication within or between sets –to tolerate other faulty caches lines and blocks. Our configuration algorithm categorizes the cache lines based on degree of conflict of their blocks to reduce the granularity of redundancy replacement. FFT-Cache thereby sacrifices a minimal number of cache lines to avoid impacting performance while tolerating the maximum amount of defects. Our experimental results on SPEC2K benchmarks demonstrate that the operational voltage is reduced down to 375mV, which achieves up to 80% reduction in dynamic power and up to 48% reduction in leakage power with small performance impact and area overhead.
Cost and detection rate of glaucoma screening with imaging devices in a primary care center

Purpose To analyze the cost and detection rate of a screening program for detecting glaucoma with imaging devices. Materials and methods In this cross-sectional study, a glaucoma screening program was applied in a population-based sample randomly selected from a population of 23,527. Screening targeted the population at risk of glaucoma. Examinations included optic disk tomography (Heidelberg retina tomograph [HRT]), nerve fiber analysis, and tonometry. Subjects who met at least 2 of 3 endpoints (HRT outside normal limits, nerve fiber index ≥30, or tonometry ≥21 mmHg) were referred for glaucoma consultation. The currently established (“conventional”) detection method was evaluated by recording data from primary care and ophthalmic consultations in the same population. The direct costs of screening and conventional detection were calculated by adding the unit costs generated during the diagnostic process. The detection rate of new glaucoma cases was assessed. Results The screening program evaluated 414 subjects; 32 cases were referred for glaucoma consultation, 7 had glaucoma, and 10 had probable glaucoma. The current detection method assessed 677 glaucoma suspects in the population, of whom 29 were diagnosed with glaucoma or probable glaucoma. Glaucoma screening and the conventional detection method had detection rates of 4.1% and 3.1%, respectively, and the cost per case detected was 1,410 and 1,435€, respectively. The cost of screening 1 million inhabitants would be 5.1 million euros and would allow the detection of 4,715 new cases. Conclusion The proposed screening method directed at population at risk allows a detection rate of 4.1% and a cost of 1,410 per case detected.
Effect of a Low–Resource-Intensive Lifestyle Modification Program Incorporating Gymnasium-Based and Home-Based Resistance Training on Type 2 Diabetes Risk in Australian Adults

OBJECTIVE—The purpose of this study was to assess the effectiveness of a low–resource-intensive lifestyle modification program incorporating resistance training and to compare a gymnasium-based with a home-based resistance training program on diabetes diagnosis status and risk. RESEARCH DESIGN AND METHODS—A quasi-experimental two-group study was undertaken with 122 participants with diabetes risk factors; 36.9% had impaired glucose tolerance (IGT) or impaired fasting glucose (IFG) at baseline. The intervention included a 6-week group self-management education program, a gymnasium-based or home-based 12-week resistance training program, and a 34-week maintenance program. Fasting plasma glucose (FPG) and 2-h plasma glucose, blood lipids, blood pressure, body composition, physical activity, and diet were assessed at baseline and week 52. RESULTS—Mean 2-h plasma glucose and FPG fell by 0.34 mmol/l (95% CI −0.60 to −0.08) and 0.15 mmol/l (−0.23 to −0.07), respectively. The proportion of participants with IFG or IGT decreased from 36.9 to 23.0% (P = 0.006). Mean weight loss was 4.07 kg (−4.99 to −3.15). The only significant difference between resistance training groups was a greater reduction in systolic blood pressure for the gymnasium-based group (P = 0.008). CONCLUSIONS—This intervention significantly improved diabetes diagnostic status and reduced diabetes risk to a degree comparable to that of other low–resource-intensive lifestyle modification programs and more intensive interventions applied to individuals with IGT. The effects of home-based and gymnasium-based resistance training did not differ significantly.
Ensemble local fractional LDA for face recognition

The classification performance of traditional LDA is often degraded by the fact that its separability criteria are not directly related to the classification accuracy in the output space. The fractional LDA (F-LDA) can solve the problem by more heavily weighting classes that are closer together. This paper proposes a novel face recognition method based on an ensemble of local F-LDA (ELF-LDA) classifiers. Firstly, an effective preprocessing scheme is employed incorporating a Logarithmic transformation and a local normalization procedure. Then the local block Gabor features are extracted by applying Gab or filters to each spatial block of preprocessed facial images. After that, multiple F-LDA classifiers are obtained on each local block of Gabor features. Finally, all the classifiers are fused to an ensemble classifier. The experimental results on CAS-PEAL-R1 face database show that our method significantly outperforms state-of-art face identification techniques. And it is noticeable that EFL-LDA obtains the best performance reported in the literature to the best of our knowledge.
Diagnostic application of padlock probes—multiplex detection of plant pathogens using universal microarrays

Padlock probes (PLPs) are long oligonucleotides, whose ends are complementary to adjacent target sequences. Upon hybridization to the target, the two ends are brought into contact, allowing PLP circularization by ligation. PLPs provide extremely specific target recognition, which is followed by universal amplification and microarray detection. Since target recognition is separated from downstream processing, PLPs enable the development of flexible and extendable diagnostic systems, targeting diverse organisms. To adapt padlock technology for diagnostic purposes, we optimized PLP design to ensure high specificity and eliminating ligation on non-target sequences under real-world assay conditions. We designed and tested 11 PLPs to target various plant pathogens at the genus, species and subspecies levels, and developed a prototype PLP-based plant health chip. Excellent specificity was demonstrated toward the target organisms. Assay background was determined for each hybridization using a no-target reference sample, which provided reliable and sensitive identification of positive samples. A sensitivity of 5 pg genomic DNA and a dynamic range of detection of 100 were observed. The developed multiplex diagnostic system was validated using genomic DNAs of characterized isolates and artificial mixtures thereof. The demonstrated system is adaptable to a wide variety of applications ranging from pest management to environmental microbiology.