Miracle-dz/UniFAR-FTUs huggingface dataset:
The Love-Hate Square Counting Method for Recommender Systems

Recommender systems provide personalized suggestions to users and are critical to the success of many e-commerce sites, such as Netflix and Amazon. Outside of e-commerce, recommender systems can be deployed in fields such as intelligence analysis, for recommending high-quality information source to analysts for further examination. In this work, we present the square counting method for rating predictions in recommender systems. Our method is based on analyzing the bipartite rating network with score-labeled edges representing user nodes' ratings to item nodes. Edges are denoted as an I-love-it or I-hate-it edge based on whether the rating score on the edge is above or below a threshold. For a target user-item pair, we count the number for each configuration of love-hate squares that involve the target pair, where the sequence of I-love-it or I-hate-it edges determine the particular configuration. The counts are used as features in a supervised machine learning framework for training and rating prediction. The method is implemented and empirically evaluated on a large-scale Yahoo! music user-item rating dataset. Results show that the square counting method is fast, simple to parallelize, scalable to massive datasets and makes highly accurate predictions. Finally, we report an interesting empirical finding that configurations with consecutive I-hate-it edges seem to provide the most powerful signal in predicting a user's love for an item.1
Effects of microphone position on snore signal quality and patient comfort

Studies on snore signal analysis have been widely conducted for detection of obstructive sleep apnea. Unfortunately, there is a lack of standardization and consensus in the signal acquisition techniques, hence weakening the diagnostic power of the studies. This paper therefore aims to explore the influence of microphone positioning on signal quality and patient comfort through objective and subjective measures, for example, sleep laboratory impulse response analysis, snore signal acquisition, and patient feedback survey. Results consistently show that the quality of snore signals, such as sound pressure level and signal-to-noise ratio, decreases above 10 dB when the mouth-to-microphone distance (MMD) changes from 0.1 m to 0.4 m, while the comfort level of patients rises with increasing MMD. Considering the trade-off between signal intelligibility and human comfort, we suggest keeping the microphone 0.3 m away from the patientpsilas mouth in order to achieve optimal signal quality with minimal patient discomfort.
On the Design and Implementation of Structured P2P VPNs

Centralized Virtual Private Networks (VPNs) when used in distributed systems have performance constraints as all traffic must traverse through a central server. In recent years, there has been a paradigm shift towards the use of P2P in VPNs to alleviate pressure placed upon the central server by allowing participants to communicate directly with each other, relegating the server to handling session management and supporting NAT traversal using relays when necessary. Another, less common, approach uses unstructured P2P systems to remove all centralization from the VPN. These approaches currently lack the depth in security options provided by other VPN solutions, and their scalability constraints have not been well studied. In this paper, we propose and implement a novel VPN architecture, which uses a structured P2P system for peer discovery, session management, NAT traversal, and autonomic relay selection and a central server as a partially-automated public key infrastructure (PKI) via a user-friendly web interface. Our model also provides the first design and implementation of a P2P VPN with full tunneling support, whereby all non-P2P based Internet traffic routes through a trusted third party and does so in a way that is more secure than existing full tunnel techniques. To verify our model, we evaluate our reference implementation by comparing it quantitatively to other VPN technologies focusing on latency, bandwidth, and memory usage. We also discuss some of our experiences with developing, maintaining, and deploying a P2P VPN.
Security of statistical databases: multidimensional transformation

The concept of multidimensional transformation of statistical databases is described. A given set of statistical output may be compatible with more than one statistical database. A transformed database <italic>D</italic>' is a database which (1) differs from the original database <italic>D</italic> in its record content, but (2) produces, within certain limits, the same statistical output as the original database. For a transformable database <italic>D</italic> there are two options: One may physically transform <italic>D</italic> into a suitable database <italic>D</italic>', or one may release only that output which will not permit the users to decide whether it comes from <italic>D</italic> or <italic>D</italic>'. The second way is, of course, the easier one. Basic structural requirements for transformable statistical databases are investigated. The closing section discusses advantages, drawbacks, and open questions.
Improved Path Exploration in shim6-based Multihoming

The shim6 host-based solution to IPv6 multihoming was designed within the IETF to provide a solution to the multihoming problem by using several IPv6 addresses per host. In this paper, we briefly describe our Linux implementation of shim6. One of the novel mechanisms introduced by shim6 is the REAchability Protocol (REAP) that allows multihomed hosts to switch to an alternate path when a failure occurs. We evaluate the performance of this protocol in a lab environment and show that it performs well in the case of unidirectional or bidirectional failures. We also show that the recovery time can be reduced by allowing REAP to send multiple probes upon failure detection.
Intelligent User Interface for Multi-Robot Search

Human-Robot Interaction (HRI) is an important part of many robotic systems, in particular systems designed for search or exploration missions. The traditional HRI methods of controlling a single robot by an operator have their shortcomings, for example the need to continuously monitor a live (synchronous) video display. When the number of robots in the system increases, the traditional methods are even harder to apply, especially when attempting to display camera imagery from multiple robots. In this work, we present an asynchronous and user-guided interface for operators of a robotic system, supporting exploration (search) of an unknown area. Focusing on Unmanned Ground Vehicles and Urban Search and Rescue missions, we describe a novel asynchronous interface and operation scheme. With our interface, the operator is able to cover the recorded imagery of a given indoor environment, by selecting points of interest on a map and watching highly-relevant images that cover it. We evaluated our interface by comparing it with the state-of-the-art asynchronous interface, and found it to perform better under certain conditions, in terms of found targets. Chapter
Characterising information correlation in a stochastic Izhikevich neuron

The Izhikevich spiking neuron model is a relatively new mathematical framework which is able to represent many observed spiking neuron behaviors, excitatory or inhibitory, by simply adjusting a set of four model parameters. This model is deterministic in nature and has achieved wide applications in analytical and numerical analysis of biological neurons due largely to its biological plausibility and computational efficiency. In this work we present a stochastic version of the Izhikevich neuron, and measure its performance in transmitting information in a range of biological frequencies. The work reveals that the deterministic Izhikevich model has a wide information transmission range and is generally better in transmitting information than its stochastic counterpart.
Detection of Parent-of-Origin Specific Expression Quantitative Trait Loci by Cis-Association Analysis of Gene Expression in Trios

Parent-of-origin (PofO) effects, such as imprinting are a phenomenon in which homologous chromosomes exhibit differential gene expression and epigenetic modifications according to their parental origin. Such non-Mendelian inheritance patterns are generally ignored by conventional association studies, as these tests consider the maternal and paternal alleles as equivalent. To identify regulatory regions that show PofO effects on gene expression (imprinted expression Quantitative Trait Loci, ieQTLs), here we have developed a novel method in which we associate SNP genotypes of defined parental origin with gene expression levels. We applied this method to study 59 HapMap phase II parent-offspring trios. By analyzing mother/father/child trios, rules of Mendelian inheritance allowed the parental origin to be defined for ∼95% of SNPs in each child. We used 680,475 informative SNPs and corresponding expression data for 92,167 probe sets from Affymetrix GeneChip Human Exon 1.0 ST arrays and performed four independent cis-association analyses with the expression level of RefSeq genes within 1 Mb using PLINK. Independent analyses of maternal and paternal genotypes identified two significant cis-ieQTLs (p<10−7) at which expression of genes SFT2D2 and SRRT associated exclusively with maternally inherited SNPs rs3753292 and rs6945374, respectively. 28 additional suggestive cis-associations with only maternal or paternal SNPs were found at a lower stringency threshold of p<10−6, including associations with two known imprinted genes PEG10 and TRAPPC9, demonstrating the efficacy of our method. Furthermore, comparison of our method that utilizes independent analyses of maternal and paternal genotypes with the Likelihood Ratio Test (LRT) showed it to be more effective for detecting imprinting effects than the LRT. Our method represents a novel approach that can identify imprinted regulatory elements that control gene expression, suggesting novel PofO effects in the human genome.
Printed in Nigeria NEW DIMENSIONS IN STUDENTS’ ASSESSMENT IN THE POST-GLOBALIZATION ERA

In the globalized and highly competitive world, sustainability of an organization largely depends on how new challenges are tackled. To successfully face these challenges, employees need certain human qualities apart from conventional academic credentials as reflected in the university grade cards. The present paper focuses on these quality aspects. Through direct survey of the university graduates in the general management courses, the present research finds out the most desirable attributes among the students by adopting a factor analysis approach. The relative importance of the factors could also be successfully assessed. The analytical process includes a questionnaire reliability analysis, factor analysis and identification of the factors. The Cronbach’s alpha calculated from relevant 20 variables was 0.710, which shows high reliability for the designed scale. To determine whether the partial correlation of the variables is small, Kaiser-Meyer-Olkin measure of sampling adequacy and Barlett’s Chi-square test of sphericity have been used before conducting the factor analysis.
The Factor Analysis Of Organizational Commitment, Job Satisfaction, And Performance Among Lecturers In Batam

This article intended to determine the important elements in commitment, satisfaction and job performance of among lecturers in universities. This study involved 179 respondents from 3 universities located in Batam, Indonesia. The population is sampled in this study. The survey instrument had used a 5-point Likert scale to obtain feedback. Overall, the survey had achieved a very high level of reliability for organizational commitment, job satisfaction, and job performance. The data obtained was processed using exploratory factor analysis (EFA) to examine the structure of the commitment, satisfaction, and performance factors. Overall the instruments are valid. Instrument used was a questionnaire. It was found, based on the EFA, that the three important factors were commitment, satisfaction, and performance. Hence, how could the findings significantly solve the problem statements, state the vision and potential of future studies. The Rotated Component Matrix showed the correlation between items and the factors after the Varimax rotation. Factor 1 contained 13 items, Factor 2 contained 12 items, while Factor 3 contained 11 items. It was found that items in Factor 1 could be under the “Commitment Factor”, items in Factor 2 could be under the “Performance Factor”, while items under factor 3 could be under the “Satisfaction Factor”. The outcome of this study could act as a guideline for lecturers to excel in their careers as well as assist institutions to develop effective evaluations.
A Workspace Decomposition for Hierarchical Motion Planning with Humanoid Robots

This paper presents a hierarchical motion planner for humanoid robots navigating complex environments. We us e a workspace decomposition that allows our planning algorit hm to be separated into high-level, subgoal, and local algorit hms. The workspace decomposition consists of a passage map, obst acle map, gradient map, and local map. We verify our approach using a virtual humanoid robot in a simulated environment.
Monitoring and management system for wireless sensor networks

Wireless sensor networks (WSN) can be used in many deployed and in many areas. For the wireless sensor network monitoring is important a tool, which is able to provide information about the network and which is able to collect and represent required data from the network. This paper describes the solution called WSN Monitor, which is able to monitor wireless sensor network. The solution is divided into the several parts such as the database storage for collect a data from a wireless sensor network, client and server side applications, Fig. 1. This paper describes the solution requirements, architecture and used technology for develop the solution. The solution was tested for real time monitoring RSS (Received Signal Strength) value in the real wireless sensor network. The second part of this paper brings describe the measurement method, input problems, the results and the evaluation of the measurement values.
Online Decoding of Hidden Markov Models for Gait Event Detection Using Foot-Mounted Gyroscopes

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used to overcome the limitation of the standard Viterbi algorithm, which does not allow the online decoding of hidden state sequences. Supervised learning of the HMM structure and validation with the leave-one-subject-out validation method were performed using treadmill gait reference data from an optical motion capture system. The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%. Overground walking tests of the HMM-based gait event detector were also successfully performed.
A Comparison of Word- and Term-based Methods for Automatic Web Site Summarization

Automatic Web site summarization is an effective means of making the content of a web site easily accessible to Web users. We demonstrate that a content-based approach to summarization, which is based on keyword and key sentence extraction from narrative text, is able to generate summaries that are as informative as human authored summaries. This work is directed towards summary generation based onn-gram terms extracted by C-value/NC-value method. Keyterm-based summaries are compared with keyword-based summaries for a list of test Web sites. The evaluation indicates that the keyterm-based method is significantly better than the keywordbased method.
B-GATA transcription factors – insights into their structure, regulation, and role in plant development

GATA transcription factors are evolutionarily conserved transcriptional regulators that recognize promoter elements with a G-A-T-A core sequence. In comparison to animal genomes, the GATA transcription factor family in plants is comparatively large with approximately 30 members. Here, we review the current knowledge on B-GATAs, one of four GATA factor subfamilies from Arabidopsis thaliana. We show that B-GATAs can be subdivided based on structural features and their biological function into family members with a C-terminal LLM- (leucine–leucine–methionine) domain or an N-terminal HAN- (HANABA TARANU) domain. The paralogous GNC (GATA, NITRATE-INDUCIBLE, CARBON-METABOLISM INVOLVED) and CGA1/GNL (CYTOKININ-INDUCED GATA1/GNC-LIKE) are introduced as LLM-domain containing B-GATAs from Arabidopsis that control germination, greening, senescence, and flowering time downstream from several growth regulatory signals. Arabidopsis HAN and its monocot-specific paralogs from rice (NECK LEAF1), maize (TASSEL SHEATH1), and barley (THIRD OUTER GLUME) are HAN-domain-containing B-GATAs with a predominant role in embryo development and floral development. We also review GATA23, a regulator of lateral root initiation from Arabidopsis that is closely related to GNC and GNL but has a degenerate LLM-domain that is seemingly specific for the Brassicaceae family. The Brassicaceae-specific GATA23 and the monocot-specific HAN-domain GATAs provide evidence that neofunctionalization of B-GATAs was used during plant evolution to expand the functional repertoire of these transcription factors.
Illusory line motion and transformational apparent motion during continuous flash suppression

A static bar is perceived to dynamically extend from a peripheral cue (illusory line motion (ILM)) or from a part of another figure presented in the previous frame (transformational apparent motion (TAM)). We examined whether visibility for the cue stimuli affected these transformational motions. Continuous flash suppression, one kind of dynamic interocular masking, was used to reduce the visibility for the cue stimuli. Both ILM and TAM significantly occurred when the d' for cue stimuli was zero (Experiment 1) and when the cue stimuli were presented at subthreshold levels (Experiment 2). We discuss that higher-order motion processing underlying TAM and ILM can be weakly but significantly activated by invisible visual information.
Mapping human brain networks with cortico-cortical evoked potentials

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.
Efficient scheduling strategies in High Performance Computing Service Platform for Shanghai Colleges

High Performance Computing Service Platform for Shanghai Colleges (HPCSP) is built to meet the mass of computing requirements of teachers and students in these colleges and universities by Shanghai Municipal Education Commission, China. HPCSP contains several high performance computing clusters, so a new efficient job scheduling strategy is required in this platform. Two level schedulers are presented in this paper, and Global Job Scheduling Algorithm (GJSA) and Local Job Scheduling Algorithm (LJSA) are given. The key feature of GJSA is taking job and node characteristics into account. LJSA is a local scheduling algorithm based on user priority and CPU number without prediction of executing time. At last GJSA and LJSA are experimentally demonstrated to reduce the Average Waiting Time of jobs and increase the utilization rate of clusters in HPCSP.
Improving Accuracy and Coverage of Data Mining Systems that are Built from Noisy Datasets: A New Model

Problem statement: Noise within datasets has to be dealt with under m ost circumstances. This noise includes misclassified data or informati on as well as missing data or information. Simple human error is considered as misclassification. The se errors will decrease the accuracy of the data mining system so it will not be likely to be used. The objective was to propose an effective algorithm to deal with noise which is represented by missing data in datasets. Approach: A model for improving the accuracy and coverage of data mining systems was proposed and the algorithm of this model was constructed. The algorithm was dealing with missing values in datasets. It splits the original dataset into two new datasets; one contains tuples that hav e no missing values and the other one contains tuples that have missing values. The proposed algor ithm was applied to each of the two new datasets. It finds the reduct of each of them and then it mer ges the new reducts into one new dataset which will be ready for training. Results: The results showed interesting as it increases the accuracy and coverage of the tested dataset compared to the traditional m odels. Conclusion: The proposed algorithm performs effectively and generates better results t han the previous ones.
Parvalbumin is freely mobile in axons, somata and nuclei of cerebellar Purkinje neurones

The Ca2+‐binding protein (CaBP) parvalbumin (PV) is strongly expressed in cerebellar Purkinje neurones (PNs). It is considered a pure Ca2+ buffer, lacking any Ca2+ sensor function. Consistent with this notion, no PV ligand was found in dendrites of PNs. Recently, however, we observed for a related CaBP that ligand‐targeting differs substantially between dendrites and axons. Thus, here we quantified the diffusion of dye‐labelled PV in axons, somata and nuclei of PNs by two‐photon fluorescence recovery after photobleaching (FRAP). In all three compartments the fluorescence rapidly returned to baseline, indicating that no large or immobile PV ligand was present. In the axon, FRAP was well described by a one‐dimensional diffusion equation and a diffusion coefficient (D) of 12 (IQR 6–20) µm2/s. For the soma and nucleus a three‐dimensional model yielded similar D values. The diffusional mobility in these compartments was ∼3 times smaller than in dendrites. Based on control experiments with fluorescein dextrans, we attributed this reduced mobility of PV to different cytoplasmic properties rather than to specific PV interactions in these compartments. Our findings support the notion that PV functions as a pure Ca2+ buffer and will aid simulations of neuronal Ca2+ signalling.