Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pilot Allocation Method of Jointing Cell Grouping and Alliance Game in Massive MIMO System

In order to improve the uplink channel estimation performance and reduce pilot contamination (PC), this paper proposes a pilot allocation method of jointing cell grouping and alliance game (JCG-AG) for massive multiple-input multiple-output (MIMO) cellular networks. It can be divided into two steps. First, by properly dividing all cells into different cell-groups, the users in different cell-groups are allocated different orthogonal pilot sets. Second, the users in the same cell-group are further divided into several mutually disjoint sub-alliances. Users belonging to different sub-alliances are allocated different pilots, and users belonging to the same sub-alliance reuse the same pilots. During the second step, the alliance formation algorithm is used to suppress PC. The JCG-AG method takes practical application as the starting point, focuses on the random distribution of users, and uses the idea of alliance game. All these make it more flexible and practical. As demonstrated in the simulation results, the JCG-AG method can greatly mitigate the PC and reduce the average mean square error (MSE) for all channel estimations in the uplink.
The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections (“Mexican hat” connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range (“inverse Mexican hat” connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.
Self-organizing maps for learning the edit costs in graph matching

Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively.
Optimal pooling strategies for laboratory testing

We consider the optimal strategy for laboratory testing of biological samples when we wish to know the results for each sample rather than the average prevalence of positive samples. If the proportion of positive samples is low considerable resources may be devoted to testing samples most of which are negative. An attractive strategy is to pool samples. If the pooled samples test positive one must then test the individual samples, otherwise they can all be assumed to be negative. The pool should be big enough to reduce the number of tests but not so big that the pooled samples are almost all positive. We show that if the prevalence of positive samples is greater than 30% it is never worth pooling. From 30% down to 1% pools of size 4 are close to optimal. Below 1% substantial gains can be made by pooling, especially if the samples are pooled twice. However, with large pools the sensitivity of the test will fall correspondingly and this must be taken into consideration. We derive simple expressions for the optimal pool size and for the corresponding proportion of samples tested.
Integrating Mobile Learning in an Undergraduate Course: An Exploration of Affordances and Challenges for Learners in UAE

This study investigated students' perceptions towards the affordances and challenges of integrating mobile learning (m-learning) into an undergraduate course. It also examined the impact of students' gender on their perceived affordances and challenges. The relationship between students' perceived affordances and perceived challenges of m-learning was also explored. Questionnaires distributed to 76 students and semi-structured interviews conducted with 17 students were used for collecting data to answer the research questions. The results revealed that students' perceptions towards the affordances of m-learning were particularly positive. They indicated that m-learning can provide an active, flexible, contextualised, and situated learning environment. However, several challenges are associated with m-learning integration. Most notable are: distraction, small device screens, plagiarism, cost, and parents' negative attitudes toward m-learning. The study also revealed that students' gender significantly impacted on the perceived affordances and challenges of m-learning. Finally, a negative significant correlation was found between mobile technology affordances and challenges.
Layout Appropriateness: A Metric for Evaluating User Interface Widget Layout

Numerous methods for evaluating user interfaces have been investigated to develop a metric that incorporates simple task descriptions which can assist designers in organizing their user interface. The metric, Layout Appropriateness (LA), requires a description of the sequences of actions users perform and how frequently each sequence is used. This task description can either be from observations of an existing system or from a simplified task analysis. The appropriateness of a given layout is computed by weighting the cost of each sequence of actions by how frequently the sequence is performed, which emphasizes frequent methods of accomplishing tasks while incorporating less frequent methods in the design. In addition to providing a comparison of proposed or existing layouts, an LA-optimal layout can be presented to the designer. The designer can compare the LA-optimal and existing layouts or start with the LA-optimal layout and modify it to take additional factors into consideration. >
Improving ASR Robustness to Perturbed Speech Using Cycle-consistent Generative Adversarial Networks

Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems. In this paper, we propose a front-end based on Cycle-Consistent Generative Adversarial Network (CycleGAN) which transforms naturally perturbed speech into normal speech, and hence improves the robustness of an ASR system. The CycleGAN model is trained on non-parallel examples of perturbed and normal speech. Experiments on spontaneous laughter-speech and creaky voice datasets show that the performance of four different ASR systems improve by using speech obtained from CycleGAN based front-end, as compared to directly using the original perturbed speech. Visualization of the features of the laughter perturbed speech and those generated by the proposed front-end further demonstrates the effectiveness of our approach.
Real-Time Sharing and Expression of Migraine Headache Suffering on Twitter: A Cross-Sectional Infodemiology Study

Background Although population studies have greatly improved our understanding of migraine, they have relied on retrospective self-reports that are subject to memory error and experimenter-induced bias. Furthermore, these studies also lack specifics from the actual time that attacks were occurring, and how patients express and share their ongoing suffering. Objective As technology and language constantly evolve, so does the way we share our suffering. We sought to evaluate the infodemiology of self-reported migraine headache suffering on Twitter. Methods Trained observers in an academic setting categorized the meaning of every single “migraine” tweet posted during seven consecutive days. The main outcome measures were prevalence, life-style impact, linguistic, and timeline of actual self-reported migraine headache suffering on Twitter. Results From a total of 21,741 migraine tweets collected, only 64.52% (14,028/21,741 collected tweets) were from users reporting their migraine headache attacks in real-time. The remainder of the posts were commercial, re-tweets, general discussion or third person’s migraine, and metaphor. The gender distribution available for the actual migraine posts was 73.47% female (10,306/14,028), 17.40% males (2441/14,028), and 0.01% transgendered (2/14,028). The personal impact of migraine headache was immediate on mood (43.91%, 6159/14,028), productivity at work (3.46%, 486/14,028), social life (3.45%, 484/14,028), and school (2.78%, 390/14,028). The most common migraine descriptor was “Worst” (14.59%, 201/1378) and profanity, the “F-word” (5.3%, 73/1378). The majority of postings occurred in the United States (58.28%, 3413/5856), peaking on weekdays at 10:00h and then gradually again at 22:00h; the weekend had a later morning peak. Conclusions Twitter proved to be a powerful source of knowledge for migraine research. The data in this study overlap large-scale epidemiological studies, avoiding memory bias and experimenter-induced error. Furthermore, linguistics of ongoing migraine reports on social media proved to be highly heterogeneous and colloquial in our study, suggesting that current pain questionnaires should undergo constant reformulations to keep up with modernization in the expression of pain suffering in our society. In summary, this study reveals the modern characteristics and broad impact of migraine headache suffering on patients’ lives as it is spontaneously shared via social media.
Learning Listener's Preference for Music Recommender System

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the listeners. In this paper, we propose music recommender system using learning listener's prefererece, such as Melon, Billboard, Bugs Music, Soribada, and Gini, with most popular current songs across all genres and styles. It is also necessary for us to make the task of calculating the preference with weight to reflect the preference of most popular current songs with its popular music charts on trends. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.
Efficient Region Tracking With Parametric Models of Geometry and Illumination

As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane; complications arise due to the fact that the object undergoes changes in pose relative to the viewing camera, in illumination relative to light sources, and may even become partially or fully occluded. We develop an efficient general framework for object tracking, which addresses each of these complications. We first develop a computationally efficient method for handling the geometric distortions produced by changes in pose. We then combine geometry and illumination into an algorithm that tracks large image regions using no more computation than would be required to track with no accommodation for illumination changes. Finally, we augment these methods with techniques from robust statistics and treat occluded regions on the object as statistical outliers. Experimental results are given to demonstrate the effectiveness of our methods.
Personal Information Leaks with Automatic Login in Mobile Social Network Services

To log in to a mobile social network service (SNS) server, users must enter their ID and password to get through the authentication process. At that time, if the user sets up the automatic login option on the app, a sort of security token is created on the server based on the user’s ID and password. This security token is called a credential. Because such credentials are convenient for users, they are utilized by most mobile SNS apps. However, the current state of credential management for the majority of Android SNS apps is very weak. This paper demonstrates the possibility of a credential cloning attack. Such attacks occur when an attacker extracts the credential from the victim’s smart device and inserts it into their own smart device. Then, without knowing the victim’s ID and password, the attacker can access the victim’s account. This type of attack gives access to various pieces of personal information without authorization. Thus, in this paper, we analyze the vulnerabilities of the main Android-based SNS apps to credential cloning attacks, and examine the potential leakage of personal information that may result. We then introduce effective countermeasures to resolve these problems.
Simulation Model of Wind Turbine Power Control System with Fuzzy Regulation by Mamdani and Larsen Algorithms

The aim of this work is to create a power control system for wind turbines based on fuzzy logic. Three power control loop was considered including: changing the pitch angle of the blade, changing the length of the blade and turning the nacelle. The stochastic law was given for changes and instant inaccurate assessment of wind conditions changes. Two different algorithms were used for fuzzy inference in the control loop, the Mamdani and Larsen algorithms. These two different algorithms are materialized and developed in this study in Matlab-Fuzzy logic toolbox which has been practically implemented using necessary intelligent control system in electrical engineering and renewable energy concepts. A comparison was done to access the functionality of the developed power control system of fuzzy logic and classical control system with PID – control. It can be concluded that the power control system of fuzzy logic allows to accurately maintain production under the control target function for each work area. When switching operation of wind turbines, it has the distinction that from 13.5 m/s to another wind velocity value, there is no overshoot and a typical of classical control systems, and when the wind velocity V is less than13.5 m / s, the pitch angle of the blades should be slightly greater than zero, and if it has increased by 5 ° , then blade length should be minimal as possible. Simulation program proved the possibility of effective power regulation for the large wind turbines controller fuzzy type on the basis of knowledge production "if - then" rules, which were shown to be effective on these wind turbines control.
4D computed tomography measurement for growing plant animation

Detailed observation of plant growth is essential for botanical analysis and realistic animation design. This study introduces spatial-temporal measurement techniques for a growing plant using X-ray Computed Tomography (CT). We scanned a target plant using CT over the course of couple of days with fixed time intervals to obtain four-dimensional (4D) volumetric images. We present a technique to segment the obtained 4D-CT images semi-automatically. We provide a 4D-CT measurement of budding bean sprouts to illustrate the feasibility of it.
Microstrip-fed quasi-Yagi antennas for WLAN applications

Novel designs of printed quasi-Yagi antennas for the 2.4 GHz WLAN band are presented. Unlike existing solutions for this category of planar antennas, the active element is now conceived as a monopole: on the one hand this allows for a direct microstrip feeding, on the other hand the antenna overall size can be reduced to less than 0. 5λ × 0. 5λ. Prototypes fabrication and measurements confirm that gains in excess of 5 dB can be obtained and a 50 Ω impedance matching bandwidth wider than 400 MHz makes the proposed design robust and compatible with the large variability in the parameters of commercial FR4 substrates.
GJB2 mutations in keratitis‐ichthyosis‐deafness syndrome including its fatal form

Keratitis‐ichthyosis‐deafness syndrome (KID; MIM 148210) is a rare congenital disorder characterized by vascularizing keratitis, sensorineural hearing loss (HL), and progressive erythrokeratoderma. Clinical variability including a fatal course of KID in the first year of life has been reported. Germline missense mutations in GJB2, encoding connexin‐26, were recently found to cause KID in 14 unrelated juvenile and adult patients. We identified a de novo GJB2 mutation G45E in a patient displaying the fatal form of the disease. No mutations were detected in five other connexin and mitochondrial genes. The G45E mutation was not reported previously in Caucasian patients but was the third most common GJB2 mutation (16% of disease alleles) in Japanese patients with autosomal recessive non‐syndromic HL. This finding suggests different modes of action of the same GJB2 mutation depending on the genetic background. This hypothesis was further substantiated by our observation of a variable clinical course in unrelated KID patients from Austria harboring the common D50N mutation in GJB2. © 2005 Wiley‐Liss, Inc.
Inventory and phylogenomic distribution of meiotic genes in Nasonia vitripennis and among diverse arthropods

The parasitoid jewel wasp Nasonia vitripennis reproduces by haplodiploidy (arrhenotokous parthenogenesis). In diploid females, meiosis occurs during oogenesis, but in haploid males spermatogenesis is ameiotic and involves a single equational division. Here we describe the phylogenomic distribution of meiotic genes in N. vitripennis and in 10 additional arthropods. Homologues for 39 meiosis‐related genes (including seven meiosis‐specific genes) were identified in N. vitripennis. The meiotic genes missing from N. vitripennis are also sporadically absent in other arthropods, suggesting that certain meiotic genes are dispensable for meiosis. Among an additional set of 15 genes thought to be specific for male meiosis in Drosophila, two genes (bol and crl) were identified in N. vitripennis and Apis mellifera (both for which canonical meiosis is absent in males) and in other arthropods. The distribution of meiotic genes across arthropods and the impact of gene duplications and reproductive modes on meiotic gene evolution are discussed.
SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three‐dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural‐network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10‐fold cross validation (Q3). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP‐winning structure‐prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3–5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics. iupui.edu/ © 2011 Wiley Periodicals, Inc. J Comput Chem, 2012
Introduction to special section on CAMRa2010: Movie recommendation in context

The challenge and workshop on Context-Aware Movie Recommendation (CAMRa2010) were conducted jointly in 2010 with the Recommender Systems conference. The challenge focused on three context-aware recommendation scenarios: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real-world online movie recommendation communities and competed against each other for obtaining the highest accuracy of recommendations. The datasets contained contextual features, such as tags, annotation, social relationsips, and comments, normally not available in public recommendation datasets. More than 40 teams from 21 countries participated in the challenge. Their participation was summarized by 10 papers published by the workshop, which have been extended and revised for this special section. In this preface we overview the challenge datasets, tasks, evaluation metrics, and the obtained outcomes.