Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity regions for multi-hop ad-hoc wireless networks using conflict graphs

Toumpis-Goldsmith introduced a strong theoretical formulation of capacity regions by computing achievable rate combinations between all pairs of communication within arbitrary topology and number of nodes. Our paper proposes a new formulation of their capacity regions with the same strong mathematical definition and results, but with less complexity computation. We use interference model of conflict graph with different levels of SINRth to reduce the complexity of computation and reach the same previous result. Our results show that for the same conditions of simulation present in previous works, we keep the same capacity regions with 75% of complexity reduction. The range of 0 to 0.61 dB represents the SINRth levels that keep more in touch with optimal results in terms of complexity and time computation. More higher values move away from optimal results.
Using LSTM Network in Face Classification Problems

Many researches have used convolutional neural networks for face classification tasks. Aiming to reduce the number of training samples as well training time, we propose to use a LSTM network and compare its performance with a standard MLP network. Experiments with face images from CBCL database using PCA for feature extraction provided good results indicating that LSTM could learn properly even with reduced training set and its performance is much better than MLP.
The value of 3-dimensional longitudinal strain in the evaluation of complex coronary lesions in non-ST-segment elevation acute coronary syndrome patient

AbstractThe aim of this study is to investigate the value of 3-dimensional global peak longitudinal strain (GPLS) derived from the 3-dimensional speckle-tracking echocardiography (3D-STE) in the diagnosis of the complex non-ST-segment elevation acute coronary syndromes (NSTE-ACS) by comparing GPLS to the synergy between percutaneous coronary intervention with taxus and cardiac surgery (SYNTAX) score.A total of 59 inpatients with NSTE-ACS in our hospital between October 2014 and January 2015 were enrolled into our study. All these subjects underwent the coronary angiography (CAG) and 3D-STE examination. The results of CAG were used to calculate the SYNTAX scores in each subject. The GPLS was assessed with speckle-tracking analysis using the dedicated software developed by GE Healthcare (Horten, Norway). We grouped all subjects according to the SYNTAX scores. A total of 23 patients (39%) were grouped as complex NSTE-ACS in our experiment. In our analysis, the values of GPLS significantly decreased from low SYNTAX scores to intermediate or high SYNTAX scores (−14.0 ± 2.7% and −9.5 ± 2.8%, respectively, P < 0.001). Multivariate regression analysis showed that GPLS and diabetes mellitus were independent predictors for complex NSTE-ACS. The area under the receiver operator characteristic curve (AUC) for GPLS to evaluate patients with complex NSTE-ACS was 0.882 (95% confidence interval [CI], 0.797–0.967, P < 0.001) with an optimal cutoff value of −11.76% (sensitivity 82.6% and specificity 83.3%). The evaluative value of the adjusted AUC for evaluating patients with complex NSTE-ACS improved after inclusion of GPLS (C statistics, 0.827–0.948, P < 0.001). The value of GPLS is significantly associated with the complexity of coronary artery lesions, according to SYNTAX score. Therefore, our study indicates that GPLS could be reproducible and efficient to evaluate the complex coronary artery disease in NSTE-ACS patients.
Design of a Power-Amplifier Feed-Forward RF Model With Physical Knowledge Considerations

In this paper, the design of a general feed-forward topology for modeling RF power amplifiers (PAs) is presented. It consists of producing the best nonlinear nonrecursive approximator of a feedback model which traduces the physical characteristics of general PAs. Following the procedure presented in this paper, the feed-forward structure, which has a polynomial form with memory (Volterra series), can be exactly determined up to the desired order of approximation. Evidently, increasing the order of the feed-forward topology results in a more complex structure, with more laborious model extraction procedures. This paper presents in detail the structures of the general nonrecursive model up to the fifth-order kernel. A systematic extraction procedure, based on standard RF laboratory continuous wave and two-tone tests, is proposed under the assumption that the PA linear output does not vary within the considered fundamental frequency band (flat behavior). Model validation is demonstrated by means of ADS simulation data and from real data measurements.
Efficient Replica Consistency Model (ERCM) for update propagation in Data Grid Environment

Data Grid Environment provides the necessary resources for data sharing in different distributed geographic locations between scientists and researchers in order to achieve common tasks which are difficult to implement in the traditional computing environments that rely on data sharing resources. In addition, data replication in Data Grid leads to better performance, availability, decreasing access time, load balancing, and fault tolerance. However, when data is modified for any replica, it must maintain the consistency for all replicas in Data Grid Environment, thus the similarity of the replicas. In addition, it must accelerate update propagation after each modification. This paper proposed a new approach called Efficient Replica Consistency Model (ERCM). The first contribution of this paper is handling optimal allocation of replica for fast access to replica upon reading data, thus minimizing job execution time. The second contribution of this paper is replica consistency and update propagation for write operations, in which most of the previous research in data grid ignored by focusing on only read operations. The simulation results showed a better performance for ERCM, when compared with existing approaches.
Effectiveness of mesothelin family proteins and osteopontin for malignant mesothelioma

Malignant mesothelioma (MM) is an aggressive tumour with poor prognosis whose early diagnosis is difficult. Mesothelin, megakaryocyte potentiating factor (MPF) and osteopontin have attracted attention as biomarkers. The aim of the present review is to provide an overview regarding these candidate biomarkers for MM, and discuss their potential role in today's clinical practice. Mesothelin and MPF have good specificity but sub-optimal sensitivity for detection of MM, being negative both in the sarcomatoid histological sub-type and in almost half of epithelioid mesothelioma, especially in the early stages. Osteopontin is a marker of the duration of asbestos exposure, but lacks specificity for mesothelioma. Several patient characteristics influence the diagnostic accuracy of biomarkers and make the establishment of the “optimal” diagnostic threshold difficult. Mesothelin and MPF have proved useful in assessing response to treatment. Combining different markers together may lead to an improvement in diagnostic accuracy, but there is still need for research in this area. Extensive validation and further research is required to improve the use of serum markers in mesothelioma management. In the near future, their application in clinical practice is probably in monitoring response to therapy, rather than in guiding diagnostic decisions and risk assessment of asbestos-exposed populations.
A Futuristic Monorail Tramway Stabilized by an Inertia Wheel

This paper presents a futuristic transportation mean: a monorail tramway, running on a narrow rail, stabilized by an inertia wheel. As the required infrastructure is very light, this transportation mean is an interesting answer to traffic saturation problems at which the agglomerations are exposed. To keep its balance, this tramway uses an inertial wheel controlled in real time, and driven by a regenerative actuator and driver, for consumption purpose. This paper is a preliminary study for this project. The analysis of the dynamics of the tramway in the transversal plane is made. The demonstration of the stabilization concept feasibility is done using the inverted pendulum, the most popular tool of the control theory, and more particularly, the inverted inertia wheel pendulum. The mechanical principles used here are classical but unique for this kind of application. Unlike in the other studies of inertia wheel pendulums, the localizations of the centers of masses were decided taking into account the real mass distribution (passengers and the tram structure) and optimized to minimize power consumption. Additionally, instead of using an encoder to measure the orientation of the pendulum, an inclinometer was preferred. Concerning the control part, the linear quadratic Gaussian control was chosen to stabilize the inverted pendulum because this control law allows controlling a system even with a partial knowledge of its state. A discussion regarding state observability is carried out showing that, even if in theory the angular velocity of the inertia wheel is enough to observe the full state of the system, in practice a sensor able to measure the orientation of the pendulum needs to be added. Experimental results are given.
miR-107 Enhances the Sensitivity of Breast Cancer Cells to Paclitaxel

Abstract Breast cancer remains the most commonly diagnosed cancer in Chinese women. Paclitaxel (PTX) is a chemotherapy medication used to treat breast cancer patients. However, a side effect of paclitaxel is the severe drug resistance. Previous studies demonstrated that dysregulation of microRNAs could regulate sensitivity to paclitaxel in breast cancer. Here, the present study aimed to lucubrate the underlying mechanisms of miR-107 in regulating the sensitivity of breast cancer cells to PTX. The results demonstrated that miR-107 was down-regulated in breast cancer tumor tissues, while TPD52 was significantly up-regulated compared with the non-tumor adjacent tissues. After confirming that TPD52 may be a major target of miR-107 via a dual-luciferase reporter assay, the western blot and RT-qPCR assays further demonstrated that miR-107 may reduce the expression level of TPD52 as well. In addition, miR-107 may prominently enhance PTX induced reduction of cell viability and the promotion of cell apoptosis in breast cancer, and the variation could be reversed by co-transfected with pcDNA3.1-TPD52. Finally, miR-107 could further reduce the decreased expression of TPD52, Wnt1, β-catenin and cyclin D1 that was induced by PTX in both mRNA and protein levels, which were rescued by pcDNA3.1-TPD52 indicating that miR-107 regulated breast cancer cell sensitivity to PTX may be targeting TPD52 through Wnt/β-catenin signaling pathway.
Prediction error method estimation for Simultaneous Localisation and Mapping

This paper presents a batch estimation method for Simultaneous Localization and Mapping (SLAM) using the Prediction Error Method (PEM). The estimation problem considers landmarks as parameter while treating dynamics using state space models. The gradient needed for parameter estimation is computed recursively using an Extended Kalman Filter (EKF). Results using simulations with a monocular camera and inertial sensors are presented and compared to a Nonlinear Least-Squares (NLS) estimator. The presented method produce both lower RMSE's and scale better to the batch length.
Intelligent traffic recommender system

In this paper we present our solution of using ICT and ubiquitous computing with the goal of reducing traffic congestion during the peek hours. The solution is in presenting intelligent recommender system that is based on the combination of several machine learning techniques used for traffic density estimation, and recommending the optimal period for accessing the crossroad. We describe the design and implementation of that system, and also perform a simulation that shows its usefulness.
Resolving abstract anaphors in Spanish discourse: Underspecification and mereological structures

Anaphoric underspecification involves multiple potential candidate antecedents for an anaphoric expression. In abstract object anaphora, where linguistic antecedents are clauses, sentences and larger fragments of discourse, the source of referential underspecification is commonly found at the propositional level. Thus, underspecified abstract anaphors have multiple antecedents of a higher-order nature (i.e., propositions and events). Following previous research on anaphoric underspecification with nominal antecedents, I propose a hypothetical three-step process toward the resolution of underspecified abstract object anaphors by hearers in discourse: (i) creation of a complex abstract object with a mereological structure that includes all potential interpretations for an anaphor, (ii) recognition of the thematic connection among propositions intended by the speaker in the form of a specific rhetorical relation, and 3) resolution of the abstract anaphor. Potential antecedents for any underspecified abstract anaphor may include atomic propositions and complex abstract referents that result from a merged interpretation of several propositions that are thematically connected. Provided that it is available, I claim that such a merged interpretation, which is part of the mereological structure, is the preferred interpretation as it is generally interpreted as part of a general purpose by the speaker, in addition to contributing to the thematic coherence of discourse.
Personal Privacy and the Web of Linked Data

This paper reports the results of a study investigating the user privacy challenges when personal data is published within linked data environments. Motivated by GetThere, a passenger information system that crowdsources transport information from users (including personal data, such as their location), four scenarios are outlined that illustrate how linked data environments can impact upon user privacy. The responsibilities of key stakeholders, including researchers, ethics committees, and the linked data community are also discussed, along with a set of guidelines designed to raise awareness of these risks and how to reduce them.
An Access Control Protocol for Embedded Devices

Embedded devices are today important elements of critical infrastructures like networking, telecommunications, electric/gas/water utility automation, and industrial automation. Access to these embedded devices should be restricted and strictly controlled. Current authentication schemes for embedded devices - mainly password based - do not provide the necessary security and scalable manageability. This paper presents the specific requirements for authentication and access control to embedded devices and proposes a novel, public-key based protocol for secure access and communication to embedded servers.
Anubis: Ultra-Low Overhead and Recovery Time for Secure Non-Volatile Memories

Implementing secure Non-Volatile Memories (NVMs) is challenging, mainly due to the necessity to persist security metadata along with data. Unlike conventional secure memories, NVM-equipped systems are expected to recover data after crashes and hence security metadata must be recoverable as well. While prior work explored recovery of encryption counters, fewer efforts have been focused on recovering integrity-protected systems. In particular, how to recover Merkle Tree. We observe two major challenges for this. First, recovering parallelizable integrity trees, e.g., Intel's SGX trees, requires very special handling due to inter-level dependency. Second, the recovery time of practical NVM sizes (terabytes are expected) would take hours. Most data centers, cloud systems, intermittent-power devices and even personal computers, are anticipated to recover almost instantly after power restoration. In fact, this is one of the major promises of NVMs. In this paper, we propose Anubis, a novel hardware-only solution that speeds up recovery time by almost 107 times (from 8 hours to only 0.03 seconds). Moreover, we propose a novel and elegant way to recover inter-level dependent trees, as in Intel's SGX. Most importantly, while ensuring recoverability of one of the most challenging integrity-protection schemes among others, Anubis incurs performance overhead that is only 2% higher than the state-of-the-art scheme, Osiris, which takes hours to recover systems with general Merkle Tree and fails to recover SGX-style trees.
Precision of ethernet measurements based on software tools

The use of Ethernet based solutions in automation applications raises the question of test tools. Free tools like Wireshark, formerly known as Ethereal, are available and offer decoding of real-time Ethernet like PROFINET IO. But is such a software based tool able to measure the required real-time constraints? What are the limits of such software based measurements? This report gives an overview of possible measurement setups and a choice of measured benchmarks.
The neuropsychological impact of insular cortex lesions

Influential models based on an increasing body of neuroimaging evidence propose that insular cortex integrates cognitive, affective, sensory and autonomic information to create a consciously perceived, ‘feeling state. ’ To appraise these models and evaluate interpretations of neuroimaging findings, the authors review evidence pertaining to the psychological and behavioural consequences of insula lesions. The authors focus on the emotional, perceptual, sensorimotor symptoms and disorders of body awareness associated with insula damage. This comprehensive review is intended to inform existing neuropsychological models of insula function in order to guide future research.
Self-Control and Pornography Behavior Among Junior High School Student

Pornography behavior by adolescents, tends to increase. This research aimed to study the influence of adolescence’s self-control on the pornography behavior. The study used a cross-sectional research design. Respondents who qualify as many as 278 boys and 322 girls. Multiple linear regression tests applied to check the effect of self-control on the pornography behavior. Result demonstrated a significant and negative correlation between selfcontrol and pornography behavior. Variable self-control also showed its influence to decrease pornography behavior. Selfcontrol is the ability of people to protect themselves from something that is not good. The self-control in adolescence, can decrease the behavior of pornography.
Licensed under Creative Commons Attribution Cc by Survey on Gaussian Estimation Based Decision Trees for Data Streams Mining

In the literature, Hoeffding tree algorithm was projected, for mining data streams decision trees became one of the most popular tools. Determine the best attribute to split the considered node is the key point of constructing the decision tree. Existing system presented the numerous methods to solve this problem. But they are either time consuming such as, in the MacDiarmid tree algorithm or justified wrongly by mathematically such as, in the Hoeffding tree algorithm. The selection of best attribute in the considered node with the help of finite data sample is similar as it would be in the case of the entire data stream with the high probability set by the user is make sure by the this method. In this paper we are presenting some efficient research approaches suggested by numerous scholars.
Sparse ReRAM Engine: Joint Exploration of Activation and Weight Sparsity in Compressed Neural Networks

Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to achieve energy efficiency for DNN inference accelerators. However, due to the tightly coupled crossbar structure, exploiting sparsity for ReRAM-based NN accelerator is a less explored area. Existing architectural studies on ReRAM-based NN accelerators assume that an entire crossbar array can be activated in a single cycle. However, due to inference accuracy considerations, matrix-vector computation must be conducted in a smaller granularity in practice, called Operation Unit (OU). An OU-based architecture creates a new opportunity to exploit DNN sparsity. In this paper, we propose the first practical Sparse ReRAM Engine that exploits both weight and activation sparsity. Our evaluation shows that the proposed method is effective in eliminating ineffectual computation, and delivers significant performance improvement and energy savings.
Gaussian Map based Acoustic Model Adaptation Using Untranscribed Data for Speech Recognition in Severely Adverse Environments

This study proposes an acoustic model adaptation scheme to improve speech recognition in severely adverse environments utilizing untranscribed data. In the proposed method, a clean GMM is estimated from clean training data, and a noise-corrupted GMM is obtained by MAP adaptation over the adaptation data. The Gaussian component of the adapted HMMs is obtained using the transform of the most similar Gaussian component of the GMM. The proposed mixture-selective model adaptation method is evaluated using an LDC corpus which represents severely adverse communication channel environments. The experimental results show the proposed adaptation method is comparable or improves performance compared to conventional MLLR adaptation. The proposed method is also effective at improving speech recognition using independent adaptation data sets. Performance results demonstrate that the proposed adaptation method is signiﬁcantly more effective at improving speech recognition in severely noise conditions, where transcribed data is unavailable and baseline ASR fails to accurately transcribe the adaptation data due to acoustic condition mismatch.
Analyzing semantic orientation of terms using Affinity Propagation

The aim of term semantic orientation analysis is to mine the sentiment polarity of words and phrases from their contexts. This paper presents a novel algorithm called Affinity Propagation to analyze semantic orientations of terms. Specifically, we build an informative graph from text corpus using an efficient Word Activation Force model and regard each term as a node in the graph. Then we propagate opinionated information over the whole graph using only a small number of seed terms. We finally utilize affinity vectors rather than context vectors to detect term polarities and construct the polarity lexicons. Evaluations on our proposed algorithm show its advantages over the state-of-the-art algorithms. And further improvements can be obtained by combining Affinity Propagation with Pointwise Mutual Information.
What is Special about HBRPs

The Parkes Multibeam Survey led to the identification of a number of long-period radio pulsars with magnetic field well above the `quantum critical field' of ~4.4×1013 G (HBRPs). Traditional pulsar emission theories postulate that radio emission is suppressed above this critical field. The aim of this project is to understand emission properties of HBRPs.
Recommendation using analysis of semantic social network in social network services

As Social Network Services became one of the most successful Web based businesses, recommendation using the social network also became actively utilized to attract and retain customers. One of the current research trends of the recommendation is using the various relationships between customers in order to increase the possibility of enhanced recommendation. This paper suggests a semantic social network that represents the various relationships among customers and products through semantic graph, and a method that generates recommendation rules through semantic social network mining over the semantic graph.