Miracle-dz/UniFAR-FTUs huggingface dataset:
A modified inversion procedure for product form of the inverse linear programming codes

This paper describes a new algorithm for the selection of the pivot row in matrix inversion when using the product form of the inverse. This algorithm has been developed for linear programming codes; however, it would be valuable for the inversion of any non-dense matrix. The procedures described in this paper have been thoroughly tested and have been in operation on the Esso Research and Engineering IBM 7090 computer for nine months. Substantial computer cost savings have been realized because of this procedure.
Full-duplex communications in interference networks under composite fading channel

In this paper, we investigate how highly dense deployments of small cells perform when full-duplex nodes communicate under composite fading channels. With regard to an arbitrary user of interest, we first identify the impact of the extra interference from co-channel users and how that compares to the intrinsic self-interference. The performance of full-duplex networks is compared to a traditional half-duplex benchmark scenario where the only source of down-link inference is originated by simultaneous transmissions of surrounding base stations. A mathematical framework based on stochastic geometry is used to carry out our investigations. Here, the system performance is assessed by considering various network configurations and distinct fading channel conditions, namely log-normal shadowing and Nakagami-m fading. Using the aforesaid framework, we approximate the aggregate interference at the user of interest and then derive closed-form expressions for the corresponding signal to interference ratio and outage probability. Results show that users in full-duplex mode achieve higher rates even though they are subject to higher interference. It is also shown that the extent of these gains depends on the self-interference cancellation value that can be achieved and how that compares to the inference from other users.
Boxify: Full-fledged App Sandboxing for Stock Android

We present the first concept for full-fledged app sandboxing on stock Android. Our approach is based on application virtualization and process-based privilege separation to securely encapsulate untrusted apps in an isolated environment. In contrast to all related work on stock Android, we eliminate the necessity to modify the code of monitored apps, and thereby overcome existing legal concerns and deployment problems that rewriting-based approaches have been facing. We realize our concept as a regular Android app called Boxify that can be deployed without firmware modifications or root privileges. A systematic evaluation of Boxify demonstrates its capability to enforce established security policies without incurring a significant runtime performance overhead.
A Hybrid Time-Series Forecasting Model Using Extreme Learning Machines

This study proposes a hybrid model which combines the linear autoregression (AR) with the nonlinear neural network (NN) based on the extreme learning machine (ELM) in an integral structure in order to improve the accuracy of time-series prediction. Unlike the developed hybrid forecasting models introduced in the literature, which usually treat the original forecasting models as a separate linear or nonlinear unit, the proposed hybrid model is an integrated model which can adapt well to both linear and non-linear situations often in periodical time series with a complicated structure. The hybrid algorithm is tested against different kinds of time series data and the results indicate that the hybrid algorithm outperforms the AR and the ELM-based neural network.
Analyzing the Impact of Queue Management Policies on the Self-Similarity of Aggregate Network Traffic

It is known that aggregate network traffic is long-range dependent and self-similar. Long-range dependent processes, unlike the theoretically popular Poisson-like short-range dependent processes, affect the queuing performance of the network drastically. The main causes of self-similarity are considered as the file sizes on the internet, human think-time and the transport protocol. The first two are statistics relying on observations and are rather difficult to intervene, however the latter can be modified to produce less self-similar processes. TCP is the dominant transport protocol and the adaptive nature of TCP congestion control mechanisms have been declared as a possible self-similarity generating factor. Moreover, the interaction between TCP congestion control mechanism and different buffer management policies affect the degree of self-similarity in different ways. Although queue management policies are currently hot research topics, their effects on self-similarity have been left as an open issue. To our best knowledge, there is only one recent work in literature which investigates the impacts of two of the queue management policies, droptail and RED on the self-similarity [1]. In our work, we include BLUE and hence provide a better comparison of queue management policies. We extend our investigation by using signal processing tools to understand the behavior of large number of TCP flows. Key-Words: Aggregate network traffic, TCP, self-similarity, Hurst estimation.
Voriconazole-Resistant Penicillium oxalicum: An Emerging Pathogen in Immunocompromised Hosts

Penicillium species are rarely reported agents of infections in immunocompromised patients. We report 3 cases of invasive mycosis caused by voriconazole-resistant Penicillium oxalicum in patients with acute myeloid leukemia, diabetes mellitus, and chronic obstructive pulmonary disease, while on voriconazole therapy. Penicillium oxalicum has not been previously recognized as a cause of invasive mycoses.
Supporting Non-Experts' Awareness of Uncertainty: Negative Effects of Simple Visualizations in Multiple Views

Video analysis tools can provide valuable datasets for a wide range of applications, such as monitoring animal populations for ecology research, while reducing human efforts for collecting information. Transferring such technology to novel application domains implies exposing non-expert users to unfamiliar datasets and technical concepts. Existing data analysis practices must adapt to the new data characteristics and technical constraints. With such changes, uncertainty is of major concern as it can yield misinterpretation of data, or distrust and rejection of valid results. We present a study of an interactive visualization of computer vision results and uncertainty. We evaluate the correctness of users' interpretation of data, and their confidence in their interpretation. We compare the impact of either data features (i.e., the true level of uncertainty) or visualization features on user perception of uncertainty. Visualization features had a similar impact on user responses than the data uncertainty itself, thus biasing user awareness of uncertainty. We conclude with the opportunities (intuitive navigation in complex unfamiliar data) and limitations (poor extrapolation and memory loss) of our visualization design which integrates simple graphs in coordinated multiple views. Our design and insights contribute to other cases where non-experts need to familiarize with novel datasets and explore their uncertainty.
Classification of Online Health Discussions with Text and Health Feature Sets

Nowadays, many health groups and forums are established on the Internet, where health consumers discuss health issues and interact with each other. Although there is a large amount of user generated content about healthcare on different social media sites, few studies have applied data mining or artificial intelligence techniques for knowledge discovery on a large scale of data in this particular emerging area. In online health forums, it is difficult for users to find relevant topics or peers due to the large amount of information. Traditional recommendation systems may not work well for health online forums, because health consumers have different intentions of participation or may be interest in different types of supports even if the content matches their interest. To help solving this problem, we apply Naïve Bayes methods in this study to classify posts and comments on QuitStop forum, which is an online community for smoking cessation intervention. Classifiers are built on different text features and health features of user quit status. Two different classification tasks are investigated: (1) classification of user intentions, and (2) classification of types of social support exchanged in interactions. We developed classifiers for posts and comments separately, and conducted experiments to compare classifiers with different text and health feature sets. It is found that using thread title or post content can achieve the highest classification accuracy on both posts and comments for user intention classification with text features. On the other hand, using the content of post or comment itself performs the best for the classification of social support types. In particular for the post, integrating health features of the post author can boost the text classifications of user intention and support type. However, user health features cannot help in improving text classifiers for the comments.
Duloxetine reduces morphine requirements after knee replacement surgery.

BACKGROUND Multimodal analgesia is advocated for perioperative pain management to reduce opioid use and its associated adverse effects. Serotonin and norepinephrine are involved in the modulation of endogenous analgesic mechanisms via descending inhibitory pain pathways in the brain and spinal cord. An increase in serotonin and norepinephrine may increase inhibition of nociceptive input and improve pain relief. Duloxetine, a selective serotonin and norepinephrine reuptake inhibitor, has demonstrated efficacy in chronic pain conditions such as painful diabetic neuropathy and post-herpetic neuralgia. The objective of the study was to evaluate the efficacy of duloxetine in reducing morphine requirements in patients after knee replacement surgery. METHODS Fifty patients received either two doses of oral duloxetine 60 mg (2 h before surgery and on first postoperative day) or placebo. All patients received patient-controlled analgesia with morphine for 48 h after operation. Pain and adverse effects were assessed at 0.5, 1, 2, 6, 12, 24, and 48 h after surgery on an 11-point numeric rating scale. RESULTS Twenty-three patients in the duloxetine group and 24 patients in the placebo group completed the study. Morphine requirements during the 48 h after surgery were significantly lower in the duloxetine group [19.5 mg, standard deviation (sd) 14.5 mg] compared with the placebo group (30.3 mg, sd 18.1 mg) (P=0.017). There were no statistically significant differences between the groups in pain scores (at rest and on movement) or in adverse effects. CONCLUSIONS Perioperative administration of duloxetine reduced postoperative morphine requirements during the first 48 h after knee replacement surgery, without significant adverse effects.
Neural System recognizes Human Pointing Gestures in Real Images

We present a neural network based system for the visual recognition of human hand pointing gestures from stereo pairs of video camera images. The accuracy of the current system allows to estimate the pointing target to an accuracy of 2 cm in a workspace area of 50x50 cm. The system consists of several neural networks arranged in a modular hierarchy. LLM networks perform the tasks of image segmentation, estimation of hand location, and estimation of 3D-pointing direction; a PSOM network yields the necessary coordinate transforms. Drawing heavily on the use of learning algorithms, the functions of all network modules were created from data examples only.
A Traffic-Aware Adaptive Sleep Mode Operation for IEEE 802.16e Based WiMAX

To enhance the energy efficiency of broadband wireless access devices based on battery power, a traffic-aware based adaptive sleep mode operation was proposed for IEEE 802.16e power saving mechanism. By analyzing the behaviors of a mobile station working in the sleep mode operation with a Markov chain model, we explore the relationship among traffic, energy consumption and delay. Based on the analytical results, we propose a heuristic algorithm to adapt the parameters of sleep mechanism to the subscriber station traffic and the delay requirement. Extensive simulation results show that the proposed algorithm outperforms the traditional power saving mechanisms in both the power consumption and the mean delay.
A stagewise rejective multiple test procedure based on a modified Bonferroni test

SUMMARY Simes (1986) has proposed a modified Bonferroni procedure for the test of an overall hypothesis which is the combination of n individual hypotheses. In contrast to the classical Bonferroni procedure, it is not obvious how statements about individual hypotheses are to be made for this procedure. In the present paper a multiple test procedure allowing statements on individual hypotheses is proposed. It is based on the principle of closed test procedures (Marcus, Peritz & Gabriel, 1976) and controls the multiple level a.
William H . Sandholm Population Games and Evolutionary Dynamics ( Economic Learning and Social Evolution )

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Work, income and gender equality in East Asia: Action guide

The guide gives a comparative overview of labour market and income differences between men and women. It explains key concepts related to equal pay, discrimination and gender equality in employment and social protection. It looks into the causes for the gender pay gap and discrimination at work. It reviews labour market instruments and shares knowledge and tools for the promotion of gender equality, more equal rewards for paid work and adequate maternity protection for all working women.
Pixel-based correspondence and shape reconstruction for moving objects

We present a method for pixel-based correspondence and shape reconstruction from multiple views of a moving object. Instead of relying on the brightness constancy assumption, this correspondence measure explicitly models intensity variations among multiple views, thus allowing estimation of lighting conditions and surface normals. The frontal-parallel assumption is not required either, since only a single intensity from each view is required for this pixel-based correspondence. The proposed shape reconstruction algorithm enforces lighting consistency and produces a final surface by fusing triangulated 3D positions and estimated surface normals without imposing smoothness constraints. Both synthetic and real experiments are conducted, where the surfaces of textureless and textured objects can be successfully reconstructed.
Android Binder Security Note On >Passing Binder Through Another Binder<

This memo describes certain details of the Android mechanism of passing binder through another binder. The aim of this paper is to describe certain technical details of this mechanism as well as to point out some obvious security weaknesses. For the sake of simplicity, we do not include description of the whole Android binder framework here. The interested reader may check the OpenBinder documentation by Dianne K. Hackborn that is still available in [1]. Despite it not being compatible with the Android binder framework, a lot of OpenBinder general ideas seem to still apply.
Understanding Motivation for Learning German among Chinese College Students: Comparing In-China and Study-Abroad Contexts

To understand motivation for learning German among Chinese college students in in-China and study-abroad (SA) contexts, the present study employed a mixed method to study 229 Chinese college students located in Beijing (N = 138) and Germany (N = 91). Quantitative data was collected through a 34-item questionnaire, and qualitative data was collected through 25 semi-structured interviews. The major findings were: 1) students in both contexts were largely integratively or instrumentally motivated to learn German, and 2) differences existed between the two groups in specific motivations, motivation level, and effects of motivation. These findings in return justify the need for research on motivation for learning not-so-important foreign languages like German.
Improving Prediction by Weighting Class Association Rules

Associative classification presents various methods whose common characteristic is the class prediction from the class association rules (rules whose consequent one is one of the class modalities). According to [11] and [10], this new approach offers better results than the traditional approaches based on rules such as the decision trees. It also offers a great flexibility with the unstructured data. However, this approach suffers from a huge mass of generated rules which leads to a waste of time and space. In this work, we propose a new associative classification method. This method is based on FCP-Growth-P, an algorithm which generates only class itemsets and integrates for pruning the specialization condition of Li. Thus one saves both execution time and storage space. The phase of classification is based on a reduced base of the most significant rules leading to each class, which ensures the speed of the method. Examples are classified using the results given by the vote of these various rules weighted by its quality measure.
Graph signal recovery from incomplete and noisy information using approximate message passing

We consider the problem of recovering a graph signal from noisy and incomplete information. In particular, we propose an approximate message passing based iterative method for graph signal recovery. The recovery of the graph signal is based on noisy signal values at a small number of randomly selected nodes. Our approach exploits the smoothness of typical graph signals occurring in many applications, such as wireless sensor networks or social network analysis. The graph signals are smooth in the sense that neighboring nodes have similar signal values. Methodologically, our algorithm is a new instance of the denoising based approximate message passing framework introduced recently by Metzler et. al. We validate the performance of the proposed recovery method via numerical experiments. In certain scenarios our algorithm outperforms existing methods.
Licensed under Creative Commons Attribution Cc by Face Recognition Using Local Ternary Pattern for Low Resolution Image

Face Recognition system is one of an effective system which is used for the identification of a person from a video frame or a digital image. The concept of FRS is primarily used in the field of security systems and hence can be get compared to other systems which are biometric in nature such as IRIS Recognition or Fingerprint systems. Apart from these techniques, Local Binary Patterns (LBP) also plays a vital role for the image recognition as well as texture recognition purposes based on Texture Spectrum Model. But, it can only develop the feature matrix from 0 and 1 on the basis of centre pixel value only. Hence, to compute upper and lower binary codes of regions, the concept of Local Ternary patterns (LTP) is being implemented in this paper. LTP is an extension to LBP which used a threshold value for selection of upper and lower binary code. In this paper, the simulations have been carried out to achieve a better improvement in the blurred images by using Discrete Wave Transformation (DWT) and LTP techniques. DWT and LTP approach for face recognition provides better FRR, FAR and Accuracy than other approaches used for low resolution images.
Visual Transmission Deficits in Mice with Targeted Disruption of the Gap Junction Gene Connexin36

In the mammalian retina, rods feed into the cone pathway through electrotonic coupling, and recent histological data suggest the involvement of connexin36 (Cx36) in this pathway. We therefore generated Cx36 null mice and monitored the functional consequences of this deficiency on early visual transmission. The homozygous mutant mice had a normally developed retina and showed no changes in the cellular organization of the rod pathway. In contrast, the functional coupling between AII amacrine cells and bipolar cells was impaired. Recordings of electroretinograms revealed a significant decrease of the scotopic b-wave in mutant animals and an increased cone threshold that is compatible with a distorted, gap junctional transmission between AII amacrine cells and cone bipolar cells. Recordings of visual evoked potentials showed extended latency in mutant mice but unaffected ON and OFF components. Our results demonstrate that Cx36-containing gap junctions are essential for normal synaptic transmission within the rod pathway.
A HYBRID ANN / HMM AUDIO-VISUAL SPEE SYSTEM

In this paper we present a system for audio-visual speech recognition based on a hybrid Artificial Neural Network/Hidden Markov Model (ANN/HMM) approach. To setup the system it was necessary to record a new audio-visual database. We will describe the recording and labeling of the database. The fusion of audio and video data is a key aspect of the paper. Three conditions, when only the audio or only the video data is reliable and when they are both equally reliable, will attract our attention. A method to combine the video and audio information based on these three conditions will be presented. An implementation of this method in an automatic fusion depending on the noise level in the audio channel is developed. The performance of the complete system is demonstrated using two types of additive noise at varying SNR.