Miracle-dz/UniFAR-FTUs huggingface dataset:
MapReduce-Based SimRank Computation and Its Application in Social Recommender System

Recently there has been a lot of interest in graph-based analysis, with examples including social network analysis, recommendation systems, document classification and clustering, and so on. A graph is an abstraction that naturally captures data objects as well as relationships among those objects. Objects are represented as nodes and relationships are represented as edges in the graph. There are many cases in which similarities among nodes are required to compute. SimRank is one of the simple and intuitive algorithms for this purpose. It is rigidly based on the random walk theorem. Existing methods on SimRank computation suffer from one limitation: the computing cost can be very high in practice. In order to optimize the computation of SimRank, a few techniques have been proposed. However, the performance of these methods are still limited by the processing ability of the single computer. Ideally, we would like to develop new parallel solutions that can offer improved processing power to compute SimRank on large data set. In this paper, we propose parallel algorithms for SimRank computation on Map-Reduce framework, and more specifically its open source implementation, Hadoop. Two different parallel methods are proposed and their performances are evaluated and compared. Furthermore, we employ the proposed methods to do the similarity computation in order to recommend appropriate products to users in social recommender systems.
Processing of soft pupae and uneclosed pharate adults of Drosophila for scanning electron microscopy

For over four decades, scanning electron microscopy (SEM) has been used in research involving Drosophila genetics and developmental biology. It allows for observation and documentation of the gross morphology of exoskeletal structures as well as their characterization at very high resolution. In most cases, SEM in Drosophila has been limited to imaging adult heads, thoraces, appendages, and embryos, as these structures are relatively hard and/or easy to process for SEM. In contrast, the structures of the pharate adult stages are difficult to prepare for SEM because their integument is quite soft, they are extremely dirty and they are resistant to the usual processing methods. Here, we present an innovative method to prepare these types of structures. Our protocol efficiently removes extraneous material originating from the exuvial fluid of pharate adults and uses a hydrophobic expansion step to keep the soft exoskeleton of the body inflated. In addition to using immersion fixation, it utilizes fixation within the body that occurs via a reaction between osmium tetroxide and alcohols that are infiltrated into the body during a hydrophobic expansion step. This novel approach results in a properly inflated integument that retains its shape in subsequent procedures. Our method provides a useful, general alternative for processing difficult samples, including soft, biological “whole‐mount” specimens and samples that are extremely dirty or resistant to fixative penetration. Microsc. Res. Tech., 2007. © 2007 Wiley‐Liss, Inc.
Performance Evaluation of Wireless IEEE 802.11b used for E-Learning Classroom Network

This paper presents an evaluation study of an IEEE 802.11b wireless LAN (WLAN) applied in E-learning classroom. The simulation is conducted using OPNET IT Guru 9.1. Also, this paper presents a simulation study to estimate the appropriate number of E-learning clients that can be supported in the WLAN as well as the user-perceived Web response time as a function of network load. Our simulation results show that an IEEE 802.11b WLAN can support up to 50 clients with modest E- learning and Web browsing activities.
Percepção de suporte organizacional, afeto positivo, afeto negativo e resiliência: antecedentes da confiança do empregado na organização

Researched more intensely starting from the decade of 90’s, trust is a variable that has found considerable prominence in the middle organizational due to its influence on the worker's behaviors. However, the employee's trust in the organization is still a construct little explored. Like this, this study had as main objective the test of a model constituted by the variables: perception of organizational support, positive affect, negative affect, resilience and the employee's trust in the organization. For that, 160 employees from public and private organizations located in Alto Paranaíba (in the state of Minas Gerais, Brazil) voluntarily answered valid and specific instruments to each construct. Data were submitted to descriptive statistics, correlations of Pearson and analyses of regression multiple stepwise, discriminating as dependent variable the employee's trust in the organization. According to the model proposed, only two variables sustained as predictors of the employee's trust in the organization. One of them was the perception of organizational support, which explained the factors of the variable criterion: “promotion of the employee's growth”, “organizational solidity ", "organizational financial recognition” and “ethical patterns”. The other variable was the resilience, which explained the factor of the variable criterion: “ethical patterns”. However, perception of organizational support has a larger predictive capacity in comparison to the only micro-systemic personal variable which remained in the model, pointing to a larger influence of the environment on the organizations behaviors rather than the individual variables. The results were discussed based on specialized literature and the conclusions refute great part of the proposed model, but it is recognized the need of more researches that can broaden knowledge concerning the variables discriminated on this study. Word-key: the employee's trust in the organization, perception of organizational support, positive affect, negative affect and resilience.
Effectiveness of routine psychotherapy: Method matters

ABSTRACT Objective: Though many studies have shown that psychotherapy can be effective, psychotherapy available in routine practice may not be adequate. Several methods have been proposed to evaluate routine psychological treatments. The aim of this paper is to demonstrate the combined utility of complementary methods, change-based benchmarking, and end-state normative comparisons, across a range of self-reported psychological symptoms. Method: Benchmarks derived from randomized controlled trials (RCTs) and normative comparisons were used to evaluate the effectiveness of psychotherapy in a large (N = 9895) sample of clients in university counseling centers (UCCs). Results: Overall, routine psychotherapy was associated with significant improvement across all symptoms examined. For clients whose initial severity was similar to RCT participants, the observed pre–post effect sizes were equivalent to those in RCTs. However, treatment tended to lead to normative end-state functioning only for those clients who were moderately, but not severely, distressed at the start of psychotherapy. Conclusions: This suggests that although psychotherapy is associated with an effective magnitude of symptom improvement in routine practice, additional services for highly distressed individuals may be necessary. The methods described here comprise a comprehensive analysis of the quality of routine care, and we recommend using both methods in concert. Clinical or methodological significance of this article: This study examines the effectiveness of routine psychotherapy provided in a large network of counseling centers. By comparing multiple established methods to define outcomes in this sample we provide a detailed understanding of typical outcomes. The findings show that, across several different problem areas, routine psychotherapy provided substantial benefit, particularly to clients in the most distress. However, there is room to improve, especially by increasing the number of clients who return to normal functioning by the end of treatment. Using distinct methods provides complementary answers to the question: How effective is routine psychotherapy?
Preliminary study of a robotic rehabilitation system driven by EMG for hand mirroring

Robotic devices can be a viable solution in different rehabilitation activities for increasing patients' gains, providing high-frequent, repetitive and interactive rehabilitation treatments. In this paper, the design, development and preliminary characterization of a robotic system for assisted hand rehabilitation, driven by surface EMG measurements, based on the mirroring of healthy hand movements is presented. The healthy hand opening and closing is detected by the muscular activity and this is used to guide a robotic glove moving the paretic hand. The innovative aspects of the research deal firstly in the contemporaneous use of EMG signals and mirroring technique and secondly in the development of an algorithm for the automatic setting of the actuators thresholds. A preliminary system characterization was conducted. The performed tests demonstrate that the system is a viable solution to allow a healthy person to perform exercises of “hand closing” “hand opening”, with ON-OFF and proportional controls, with a success rate in tests carried out by 98%. The proposed system is a starting point for a novel approach to hand mirroring rehabilitation on patients with upper-limb motor deficits.
Personalized web recommendations analyzing sequential behaviour using implicit data streams: A survey

The ongoing expansion of commercial websites greatly increases the need of effective recommender systems for finding the solution to information overload problem. Conventional systems made use of explicit information obtained from users in the form of ratings and feedback queries which are highly affected by users' demographic location, age and attitude. The methodology used nowadays is to make use of implicit information of users to remove this constraint. This implicit data can be users surfing behavior, personal information or contextual situations that can be used to improve the accuracy of recommendations. This article provides a review on recent developments in making recommendations based on data that is extracted by monitoring the surfing behavior of customers and applying various information filtering approaches. The domain used in the study is click stream data of e-commerce website. We compare and evaluate available algorithms and examine their role in future developments.
Failure Detector-Ring Paxos Based Atomic Broadcast Algorithm

An atomic broadcast problem constitutes an essential component in fault-tolerant distributed systems. an atomic broadcast algorithm ensures that all processes deliver the same messages sequence. Many algorithms solving the atomic broadcast problem have been published. Recently, a new Paxos-based atomic broadcast algorithm has been proposed for clustered systems, named Ring Paxos. It inherits many of Paxos characteristics: safe under asynchronous assumptions, live under weak synchronous assumptions, and requires a majority of non-faulty processes to ensure progress. However, the proposed algorithm relies on strong assumptions, and uses the group membership for faults tolerance. Recently, a new failure detector Ring Paxos-based atomic broadcast algorithm (FD-RP) for asynchronous distributed systems have been proposed. It inherits some of Ring Paxos characteristics and uses failure detectors to tolerate failures instead of a membership service. The aim of this paper is to complete the work done in our previous work by evaluating the performance of the FD-RP protocol by simulation and compare it with the Ring Paxos protocol, results show that the proposed protocol performs better than the Ring Paxos protocol in some situation and is recommended in applications with low latencies.
Smart sensors: Physics, technology and applications

The semiconductor technology has advanced a rapid progress in the sensors which are heart of instrumentation systems. These days, most of the sensors are ‘smart’ in nature. In such sensors, the sensing elements and associated electronics are integrated on the same chip. Generally, silicon is preferred as sensor material because it can give a suitable physical conversion effect and can give suitable signal processing circuit, with integration of the sensing element and electronic circuits on the chip, by using standard planar technology. The smart sensors have main advantages of fast signal conditioning, higher signal-to-noise ratio, self testing, auto calibration, small physical size, high reliability, failure prevention and detection. In the present paper, recent advances in smart sensors along with their design and fabrication aspects, are discussed. The smart sensors developed in this Laboratory, mainly smart pressure sensors and smart lithotripsy sensor, are presented, besides smart sensors used in industrial controls, automotive and biomedical fields. Biochips and biosensors are also cited for some of the specific applications.
Vertical Semantic Discovery: The SICH Approach on NPS

The SICH Project specific objective is to develop new strategic tools and assessment techniques, based on semantic analysis on texts, to support the dynamic mapping and the automatic identification of illegal content over the Net. Xenophobia/Racism, illegal online Gambling and Novel Psychoactive Substances (NPS) are the main areas of reference. In this paper the authors present a systems based on a semantic engine running an ontology, based on a criminological analysis of NPS phenomenon. The system, after crawling the web, classifies key elements of NPS crime, not necessarily representing illegal content or criminal activity evidence. Thanks to this approach, the system aims to dramatically reduce the learning time of the key factors of illegal activities, with further reuse of well established web graph and SNA tools to highlight the relevant aspects of the semantic results.
Input injection detection in Java code

Input Injections are considered as the most common and effective vulnerabilities to exploit in many software systems (esp. web apps). In this paper, we propose a way to detect such vulnerabilities, such as SQL injection, command injection, and cross-site scripting. Input injection is caused by executing user inputs which have not been validated or sanitized, so that the purpose of execution is changed by malicious agents into their advantages. The input injection detector is done by extending an existing static analysis tool, namely FindBugs. The detection uses a dataflow analysis to monitor user-contaminated variables. To improve accuracy, reducing false positives and false negatives, dataflow analysis is used to monitor variables that have been validated or sanitized by developers. Our detector has only few false positives and false negatives based on our testing using our test cases and existing applications, i.e. WebGoat and ADempiere.
Estimation of Forest Plantation Productivity Using a Physiologically Based Model Driven with Meteorological Data and Satellite-derived Estimates of Canopy Photosynthetic Capacity

Within the framework of Kyoto protocol, carbon sink concept is a major topic for the global climate change. There is an urgent need to collect some necessary data on the forest plantations. Therefore, there is a clear need to develop methods for estimating the biomass at diverse sites simply with non- destructive methods. Modeling is one of effective approaches and Process Based Models (PBMs) can provide a better understanding of stand growth and dynamics. A one simplified model of PBMs is 3PG model and modified one is 3PGS model; Physiological Principles Predicting Growth with Satellite data (1). The model enables to use parameters derived from satellite data such as Fraction of absorbed Photosynthetically Active Radiation (FPAR) as well as meteorological data. The objective of this study is to compare between 3PG model and 3PGS model by estimating above ground biomass (Wabv). Landsat Enhanced Thematic Mapper-Plus (ETM+) data were used for the model implementation. The estimated parameters were validated with the ground-measured values. Field measurements were conducted in Eucalyptus camaldulensis and Acacia mangium plantation. The 3PGS model outputs showed that there was very good relationship between measured (ground-based measurements) and simulated (modeled) values. Root mean square errors (RMSE) of 3PGS model results were less than the RSME of 3PG model results in Eucalyptus plot. The result applied for Acacia mangium showed it had higher RMSE between measured and simulated values than the result applied for Eucalyptus camaldulensis. The results indicate that the 3PGS model can provide reasonable estimates of Wabv.
Publication bias in orthopaedic research: an analysis of scientific factors associated with publication in the Journal of Bone and Joint Surgery (American Volume).

BACKGROUND Positive outcomes are common in the orthopaedic literature, and there are many who believe it may be due to the preferential publication of studies with positive findings-a phenomenon known as publication bias. The purpose of this investigation was to determine whether positive findings rendered a manuscript submitted to The Journal of Bone and Joint Surgery (American Volume) more likely to be accepted for publication. METHODS A total of 1181 manuscripts submitted to The Journal of Bone and Joint Surgery between January 1, 2004, and June 30, 2005, for publication as scientific articles were analyzed, with 855 meeting the inclusion criteria. The direction of the study findings (positive, neutral, or negative) was independently graded by three blinded reviewers. The final disposition (acceptance or rejection) was recorded, as was information on the scientific characteristics plausibly related to acceptance or rejection. Logistic regression was used to identify factors associated with acceptance for publication. RESULTS The overall acceptance rate was 21.8% (186 of 855 studies). The study outcome was positive for 72.5% (620) of the manuscripts. The acceptance rate for the 235 manuscripts with nonpositive findings was 23.0% (fifty-four studies) compared with 21.3% (132) of the 620 studies with positive findings (crude odds ratio, 1.10 [95% confidence interval, 0.77 to 1.58]; p = 0.593). After controlling for all covariates, the adjusted odds ratio was 0.92 (95% confidence interval, 0.62 to 1.35; p = 0.652). In the multivariate analysis, the only factor significantly associated with acceptance for publication was level of evidence (p = 0.001). CONCLUSIONS We found no evidence of publication bias in the review of manuscripts for publication by The Journal of Bone and Joint Surgery, as positive and nonpositive studies were accepted at similar rates. The dearth of nonpositive studies in the orthopaedic literature is of concern, and may be due largely to investigator-based factors. Orthopaedic researchers should submit negative and neutral studies for publication, confident that the likelihood of acceptance will not be influenced by the direction of study findings.
A language modeling approach for the recommendation of tourism-related services

In a ubiquitous scenario, people are typically confronted with context evolution and changing influences. This may create new needs and may condition the user perception of what is relevant information. Over the years, different approaches have been proposed to design personalized Recommender Systems (RS), but state-of-the-art approaches mostly assume a fixed representation of a user profile; the dynamicity of the user's interests (and the way of expressing them) while interacting with the environment is not considered. Aim of this work is to predict a user's preferences in the tourism domain, to provide personalized and context-aware recommendations. Therefore, we define a user profile model which expresses in a formal way the user's opinions with respect to a particular entity. In particular, the proposed approach formally models the user generated content (UGC) connected to a group of reviews (written by expert users) for each entity, and compares it with a (positive and negative) statistical language model representing the target user profile associated with that entity. The effectiveness of the approach is illustrated on a real-case scenario.
Plant species classification using flower images—A comparative study of local feature representations

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification.
Chapter 5 Feature-based Methods in 3 D Shape Analysis

The computer vision and pattern recognition communities have recently witnessed a surge in feature-based methods for numerous applications including object recognition and image retrieval. Similar concepts and analogous approaches are penetrating the world of 3D shape analysis in a variety of areas including non-rigid shape retrieval and matching. In this chapter, we present both mature concepts and the state-of-the-art of feature-based approaches in 3D shape analysis. In particular, approaches to the detection of interest points and the generation of local shape descriptors is discussed. A wide range of methods is covered including those based on curvature, those based on difference-of-Gaussian scale space, and those that employ recent advances in heat kernel methods.