Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Prediction of Social Network Links Using Implicit Sources of Information

In this paper, we introduce a supervised machine learning framework for the link prediction problem. The social network we conducted our empirical evaluation on originates from the restaurant review portal, yelp.com. The proposed framework not only uses the structure of the social network to predict non-existing edges in it, but also makes use of further graphs that were constructed based on implicit information provided in the dataset. The implicit information we relied on includes the language use of the members of the social network and their ratings with respect the businesses they reviewed. Here, we also investigate the possibility of building supervised learning models to predict social links without relying on features derived from the structure of the social network itself, but based on such implicit information alone. Our empirical results not only revealed that the features derived from different sources of implicit information can be useful on their own, but also that incorporating them in a unified framework has the potential to improve classification results, as the different sources of implicit information can provide independent and useful views about the connectedness of users.
ML-performance of low-density parity check codes

In this paper, we derive the maximum likelihood (ML) performance for low-density parity check (LDPC) codes, considering BPSK and QPSK transmission over a Gaussian channel. We compare the theoretical ML performance with the performance of the iterative decoding algorithm. It turns out that the performance of the iterative decoding algorithm is close to the ML performance when the girth of the code is sufficiently high. When the girth of the code is equal to or smaller than 6, the decoding algorithm performs sub-optimal: the bit error rate (BER) obtained with the iterative decoding algorithm is much higher than the optimal BER that can be obtained when using ML decoding.
Directivity and noise reduction in hearing aids: speech perception and benefit

Abstract Hearing aid. Aim To compare the performance, benefit and satisfaction of users of ITE, CIC and BTE digital hearing aid with noise reduction and omnidirectional and directional microphones. Method 34 users of hearing aid were evaluated by means of speech perception in noise tests and APHAB and IOI self assessment questionnaires. Prospective study. Results Better results were obtained by users of ITE, CIC and directional hearing aids, however, no statistical significance was found between the groups. Conclusion Directivity improved speech perception in noise and benefit in daily life situations.
Effects of the STOP and LIST Strategy on the Writing Performance of Struggling Fourth Graders

This study tested the effectiveness of a writing planning strategy (STOP & LIST) with four struggling students from fourth grade. A multiple-baseline design (AB) was used with baselines consisting of between five and seven daily probes. The duration of the intervention was between five and seven 25-minute sessions. A randomization procedure was implemented within the constraint that the baseline had to comprise at least five measurements and the treatment had to comprise at least three. The data were analyzed using visual inspection, different effect sizes, randomization tests, and piecewise regression analyses. Results revealed distinct improvements in both the length and quality of stories that the participants produced from baseline to the end of the training. This indicates that the ability of young struggling writers to plan narratives can be fostered considerably through rather simple means. Implications for practice and future research are discussed.
H D 3 cooling in primordial gas

Simulations of the thermal and dynamical evolution of primordial gas typically focus on the role played by H2 cooling. H2 is the dominant coolant in low-density primordial gas and it is usually assumed that it remains dominant at high densities. However, H2 is not an effective coolant at high densities, owing to the low critical density at which it reaches local thermodynamic equilibrium and to the large opacities that develop in its emission lines. It is therefore important to quantify the contribution made to the cooling rate by emission from the other molecules and ions present in the gas. A particularly interesting candidate is the H C 3 ion, which is known to be an effective coolant at high densities in planetary atmospheres. In this paper, we present results from simulations of the thermal and chemical evolution of gravitationally collapsing primordial gas, which include a detailed treatment of H C 3 chemistry and an approximate treatment of H C 3 cooling. We show that in most cases, the contribution from H C 3 is too small to be important, but if a sufficiently strong ionizing background is present, then H C 3 cooling may become significant.
Modeling Stress Using Thermal Facial Patterns: A Spatio-temporal Approach

Stress is a serious concern facing our world today, motivating the development of better objective understanding using non-intrusive means for stress recognition. The aim for the work was to use thermal imaging of facial regions to detect stress automatically. The work uses facial regions captured in videos in thermal (TS) and visible (VS) spectrums and introduces our database ANU StressDB. It describes the experiment conducted for acquiring TS and VS videos of observers of stressed and not-stressed films for the ANU StressDB. Further, it presents an application of local binary patterns on three orthogonal planes (LBP-TOP) on VS and TS videos for stress recognition. It proposes a novel method to capture dynamic thermal patterns in histograms (HDTP) to utilize thermal and spatio-temporal characteristics associated in TS videos. Individual-independent support vector machine classifiers were developed for stress recognition. Results show that a fusion of facial patterns from VS and TS videos produced significantly better stress recognition rates than patterns from only VS or TS videos with p <; 0.01. The best stress recognition rate was 72% and it was obtained from HDTP features fused with LBP-TOP features for TS and VS videos respectively.
Improved Performance with PID Filter Controllers for Unstable Processes Involving Time Delays

Using the direct synthesis method, a proportional-integral-derivative (PID) controller in series with a lead- lag filter is designed for control of the open-loop unstable processes with time delay. Pade's 2nd order approximation for the time delay term is considered for obtaining the controller structures of PID. Study of the performance of the designed controllers has been carried out on various unstable processes. Set-point weighting is considered to reduce the overshoot. The proposed scheme consists of only one tuning parameter, and systematic guidelines are provided for selection of the tuning parameter based on the peak value of the sensitivity function. Robustness analysis has been carried out based on small gain theorem. Nominal and robust control performances are achieved with the proposed method and improved closed loop performances are obtained when compared to the recently reported methods in the literature.
The Visible universe at the light of modern statistical physics

In the last years there has been a growing interest in the understanding a vast variety of scale invariant and critical phenomena occurring in nature. Experiments and observations indeed suggest that many physical systems develop spontaneously correlations with power law behaviour both in space and time. Pattern formation, aggregation phenomena, biological systems, geological systems, disordered materials, clustering of matter in the universe are just some of the fields in which scale invariance has been observed as a common and basic feature. However, the fact that certain structures exhibit fractal and complex properties does not tell us why this happens. A crucial point to understand is therefore the origin of the general scale-invariance of natural phenomena. This would correspond to the understanding of the origin of fractal structures and of the properties of Self- Organized Criticality (SOC) from the knowledge of the microscopic physical processes at the basis of these phenomena. Fractal geometry can play a crucial role in extracting the correct physical properties from experimental data. In particular, the recent availability of complete three dimensional samples of galaxies and clusters permits a direct study of their spatial properties. We present a brief review of galaxy correlations based on the methods of modern Statistical Physics. These methods are able to identify self-similar and non-analytical properties, and allow us to test the usual homogeneity assumption of luminous matter distribution. The new analysis shows that all the available data are consistent with each other and show fractal correlations (with dimension $D \simeq 2$) up to the deepest scales probed until now ($1000 \hmp$)
Development of an experimental model of endometriosis using mice that ubiquitously express green fluorescent protein.

BACKGROUND Aiming at improving an animal model of endometriosis, we developed a homologous mouse model using 'green mice' that ubiquitously express green fluorescent protein. METHODS Endometrial fragment obtained from estradiol (E2)-supplemented ovariectomized 'green mice' was minced and injected into the peritoneal cavity of ovariectomized wild-type mice. The recipient wild mice were raised with or without E2 supplementation for 2 weeks, and then were euthanized. Endometriotic lesions that developed in the abdomen were examined both macroscopically and microscopically under fluorescence, and weight of the lesions was measured. RESULTS The endometriotic lesions were more clearly detected under fluorescence imaging than by conventional macroscopic examination. Histologically, endometriotic lesions deriving from 'green mice' were sharply distinguished from surrounding host tissues under fluorescence microscopy. More lesions developed in E2-supplemented than control recipient mice. The measured fluorescence intensity of endometriotic lesions showed significant positive correlation with their weight (R=0.844, P<0.0001), and was significantly higher in E2-supplemented mice than in vehicle-supplemented mice (P=0.0062). CONCLUSION The present endometriosis model using 'green mice' would be useful for expeditious identification and quantitative evaluation of endometriotic lesions.
Expression of sialyl-Tn epitopes on β1 integrin alters epithelial cell phenotype, proliferation and haptotaxis

Sialyl-Tn (STn) is a tumor-associated carbohydrate antigen overexpressed in various carcinomas. To obtain its expression, murine carcinoma cells were transfected with the cDNA encoding ST6GalNAc I, a glycosyltransferase that acts exclusively on O-glycans. Overexpression of this enzyme led to the expected expression of cell surface STn epitopes. Surprisingly, the transfectants (STn+ cells) presented dramatic morphological changes and altered behavior. These STn+ cells lost the epithelial appearance of parental cells, became larger, more elongated and presented disorganized actin stress fibers. Additionally, their proliferation was impaired and their ability to migrate on fibronectin and hyaluronic acid was severely reduced. By contrast their adhesion on fibronectin remained unchanged. The major glycoprotein carrying the STn epitope was shown to be the integrin β1 subunit. Anti-STn antibodies could restore migration of STn+ cells on fibronectin. A constitutively active permeant form of RhoA (TAT-RhoAVal-14) also restored motility on fibronectin of STn+ cells as well as a parental STn-cellular phenotype. These observations indicate that overexpression of ST6GalNAc I leads to a major change of the O-glycosylation of the integrin β1 chain which in turn impairs the integrin-mediated signalling and leads to major alterations in morphology and cell behavior.
Automatic facial expression analysis

Humans spend a large amount of their time interacting with computers of one type or another. However, computers are emotionally blind and indifferent to the affective states of their users. Human-computer interaction which does not consider emotions, ignores a whole channel of available information. Faces contain a large portion of our emotionally expressive behaviour. We use facial expressions to display our emotional states and to manage our interactions. Furthermore, we express and read emotions in faces effortlessly. However, automatic understanding of facial expressions is a very difficult task computationally, especially in the presence of highly variable pose, expression and illumination. My work furthers the field of automatic facial expression tracking by tackling these issues, bringing emotionally aware computing closer to reality. Firstly, I present an in-depth analysis of the Constrained Local Model (CLM) for facial expression and head pose tracking. I propose a number of extensions that make location of facial features more accurate. Secondly, I introduce a 3D Constrained Local Model (CLM-Z) which takes full advantage of depth information available from various range scanners. CLM-Z is robust to changes in illumination and shows better facial tracking performance. Thirdly, I present the Constrained Local Neural Field (CLNF), a novel instance of CLM that deals with the issues of facial tracking in complex scenes. It achieves this through the use of a novel landmark detector and a novel CLM fitting algorithm. CLNF outperforms state-of-the-art models for facial tracking in presence of difficult illumination and varying pose. Lastly, I demonstrate how tracked facial expressions can be used for emotion inference from videos. I also show how the tools developed for facial tracking can be applied to emotion inference in music.
An efficient interpolation-based systematic encoder for low-rate Blaum-Roth codes

In this paper, we propose an efficient interpolation-based systematic encoder for low-rate Blaum-Roth codes. Our algorithm is based upon an equivalent definition of [p, k] Blaum-Roth codes from the perspective of generator matrices. Moreover, applying the interpolation method first proposed by D.J.J. Versfeld et al. to the generator matrix, we then derive a formula to resolve the erasure-only decoding problem. Finally, we present a straightforward systematic encoder based on this formula. Compared to the encoders in [5] and [14], it is more efficient for low-rate codes.
Pressure laws and fast Legendre transform

In this paper we investigate algorithms based on the Fast Legendre Transform (FLT) in order to compute tabulated Equation Of State (EOS) for fluids with phase transition. The equation of state of a binary mixture is given by an energy minimization principle. According to the miscible or immiscible nature of the mixture, the energy of the system is either a convex envelope or an inf-convolution of the energies of the two phases. Because these operations are closely linked to Legendre transform, it is possible to construct fast algorithms that compute efficiently these operations. In addition, it appears that the natural mathematical tool for studying mixture thermodynamics in the Legendre space is the max-plus algebra theory.
A formation control framework based on Lyapunov approach

A general control framework for a formation system composed of a team of nonholonomic wheeled mobile robots (WMRs) considering the formation dynamics under static connection structure but subject to dynamic connection stability is proposed in this paper. Hence, a rigorous formation theory is obtained essentially based on the differential structure of the formation system. With this result, a case design using a Lyapunov based control complying with the proposed formation theory is provided to demonstrate the capability and feasibility of the proposed framework. Finally, the wander-mode simulation with three WMRs is conducted while the formation configuration may be changing on-line. It is believed that the present results can be further extended to many applications involving the nonlinear multi-agent system.
Context-Based Zero-Interaction Pairing and Key Evolution for Advanced Personal Devices

Solutions for pairing devices without prior security associations typically require users to actively take part in the pairing process of the devices. Scenarios involving new types of devices like Internet-of-Things (IoT) appliances and wearable devices make it, however, desirable to be able to pair users' personal devices without user involvement. In this paper, we present a new approach for secure zero-interaction pairing suitable for IoT and wearable devices. We primarily require pairing to happen between "correct" devices -- the devices that the user intends to pair. Our pairing scheme identifies the correct devices based on measuring sustained co-presence over time. We do this by having the devices compute a fingerprint of their ambient context using information gathered through commonly available sensor modalities like ambient noise and luminosity. We introduce a novel robust and inexpensive approach for fingerprinting contexts over time. Co-present devices will observe roughly similar context fingerprints that we use in a key evolution protocol to gradually increase the confidence in the authenticity of the correct devices. Our experiments show the effectiveness of this approach for zero-interaction pairing.
A Beacon-based Docking System for an Autonomous Mobile Robot

We propose an autonomous docking system for a mobile robot which helps the robot to dock at a specific location with a particular orientation. This system uses active IR beacons which transmit infrared signals all through out the workspace. The beacons are placed in such a way that the docking target forms the midpoint of the line joining the beacons. The robot is equipped with an Ultrasonic Range Meter (SRF05) and a set of infrared receivers. The robot, using all its sensors, detects the beacons and successfully reaches the docking-target with the required orientation. Several experiments are conducted and the results prove the efficacy of the docking system.
Modeling and simulation of ship longitudinal motion

A method of studying hydrodynamic coefficient's nonlinear adaptive intelligent model was proposed based on ship motion in the sea all-weather situation. On the basis of microtome section theory, giving the hydrodynamic expression of different wave direction. Using optimal stopping method to optimize the radial basis function(RBF), through simulate and research get the real-time controlling model of ship longitudinal motion based on water pool experiment and the analysis of hydrodynamic coefficient of the 442T ship representative sea situation. This model can on-line compute hydrodynamic coefficients of ship longitudinal motion which has irrespective of frequency in any direction, speed and sea situation, so that we can get the on-line all-weather controlling model of ship longitudinal motion. Simulation results indicate that nonlinear coefficient model can be quickly and accurately established by using optimal stopping algorithm of radial basis function. Research on intelligent model was done by different and columns methods: full-samples RBF model, which the maximal relative error is 9.1289% under the arbitrary ocean condition degree, navigating speed and navigate angle; the model's simulation characteristic get better after classified database, the maximal relatively error of which was 7.9467%;the maximal relatively error down to 4.7873 % because of gradient algorithm introduced and the mixed intelligent model was established in three class by classified data, the maximal relatively error of which was 0.9845%.
Dialog state tracking with attention-based sequence-to-sequence learning

We present an advanced dialog state tracking system designed for the 5th Dialog State Tracking Challenge (DSTC5). The main task of DSTC5 is to track the dialog state in a human-human dialog. For each utterance, the tracker emits a frame of slot-value pairs considering the full history of the dialog up to the current turn. Our system includes an encoder-decoder architecture with an attention mechanism to map an input word sequence to a set of semantic labels, i.e., slot-value pairs. This handles the problem of the unknown alignment between the utterances and the labels. By combining the attention-based tracker with rule-based trackers elaborated for English and Chinese, the F-score for the development set improved from 0.475 to 0.507 compared to the rule-only trackers. Moreover, we achieved 0.517 F-score by refining the combination strategy based on the topic and slot level performance of each tracker. In this paper, we also validate the efficacy of each technique and report the test set results submitted to the challenge.
Named entity extraction and disambiguation for informal text: the missing link

Social media content represents a large portion of all textual content appearing on the Internet. These streams of user generated content (UGC) provide an opportunity and challenge for media analysts to analyze huge amount of new data and use them to infer and reason with new information. An example of a main sector for social media analysis is the area of customer feedback through social media. With so many feedback channels, organizations can mix and match them to best suit corporate needs and customer preferences. Another beneficial sector is social security. Automatic monitoring and gathering of information posted in social media can be helpful to take actions to prevent violent and destructive behavior. A main challenge of natural language is its ambiguity and vagueness. To automatically resolve ambiguity by computers, the grammatical structure of sentences is used, for instance, which groups of words go together (phrases) and which words are the subject or object of a verb. However, when we move to informal language widely used in social media, the language becomes more ambiguous and thus more challenging for automatic understanding. Information Extraction (IE) is the research field that enables the use of unstructured text in a structured way. Named Entity Extraction (NEE) is a sub task of IE that aims to locate phrases (mentions) in the text that represent names of entities such as persons, organizations or locations regardless of their type. Named Entity Disambiguation (NED) is the task of determining which correct person, place, event, etc. is referred to by a mention. The main goal of this thesis is to mimic the human way of recognition and disambiguation of named entities especially for domains that lack formal sentence structure. The proposed methods open the doors for more sophisticated applications based on users’ contributions on social media. We propose a robust combined framework for NEE and NED in semi-formal and informal text. The achieved robustness has been proven to be valid across languages and domains and to be independent of the selected extraction and disambiguation techniques. It is also shown to be robust against shortness in labeled training data and against the informality of the used language. We have discovered a reinforcement effect and exploited it a technique that improves extraction quality by feeding back disambiguation results. We present a method of handling the uncertainty involved in extraction to improve the disambiguation results. A generic approach for NED in tweets for any named entity (not entity oriented) is presented. This approach overcomes the problem of limited coverage of KBs. Mentions are disambiguated by assigning them to either a Wikipedia article or a home page. We also introduce a method to enrich the limited entity context.