Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Power Allocation for Three-phase Bidirectional DF Relaying with Fixed Rates

Wireless systems that carry delay-sensitive information (such as speech and/or video signals) typically transmit with fixed data rates, but may occasionally suffer from transmission outages caused by the random nature of the fading channels. If the transmitter has instantaneous channel state information (CSI) available, it can compensate for a significant portion of these outages by utilizing power allocation. In this paper, we consider optimal power allocation for a conventional dual-hop bidirectional decode-and-forward (DF) relaying system with a three-phase transmission protocol. The proposed strategy minimizes the average power consumed by the end nodes and the relay, subject to some maximum allowable system outage probability (OP), or equivalently, minimizes the system OP while meeting average power constraints at the end nodes and the relay. We show that in the proposed power allocation scheme, the end nodes and the relay adjust their output powers to the minimum level required to avoid outages, but will sometimes be silent, in order to conserve power and prolong their lifetimes. For the proposed scheme, the end nodes use the instantaneous CSI of their respective source-relay links and the relay uses the instantaneous CSI of both links.
A Network Positioning System for the Internet

Network positioning has recently been demonstrated to be a viable concept to represent the network distance relationships among Internet end hosts. Several subsequent studies have examined the potential benefits of using network position in applications, and proposed alternative network positioning algorithms. In this paper, we study the problem of designing and building a network positioning system (NPS). We identify several key system-building issues such as the consistency, adaptivity and stability of host network positions over time. We propose a hierarchical network positioning architecture that maintains consistency while enabling decentralization, a set of adaptive decentralized algorithms to compute and maintain accurate, stable network positions, and finally present a prototype system deployed on PlanetLab nodes that can be used by a variety of applications. We believe our system is a viable first step to provide a network positioning capability in the Internet.
Modeling Human Aspects to Enhance Software Quality Management

The aim of the research is to explore the impact of cognitive biases and social networks in testing and developing software. The research will aim to address two critical areas: i) to predict defective parts of the software, ii) to determine the right person to test the defective parts of the software. Every phase in software development requires analytical problem solving skills. Moreover, using everyday life heuristics instead of laws of logic and mathematics may affect quality of the software product in an undesirable manner. The proposed research aims to understand how mind works in solving problems. People also work in teams in software development that their social interactions in solving a problem may affect the quality of the product. The proposed research also aims to model the social network structure of testers and developers to understand their impact on software quality and defect prediction performance.
Application of a Cognitive-Inspired Algorithm for Detecting Communities in Mobility Networks

The emergence and the global adaptation of mobile devices has influenced human interactions at the individual, community, and social levels leading to the so called Cyber-Physical World (CPW) convergence scenario [1]. One of the most important features of CPW is the possibility of exploiting information about the structure of the social communities of users, revealed by joint movement patterns and frequency of physical co-location. Mobile devices of users that belong to the same social community are likely to "see" each other (and thus be able to communicate through ad-hoc networking techniques) more frequently and regularly than devices outside the community. In mobile opportunistic networks, this fact can be exploited, for example, to optimize networking operations such as forwarding and dissemination of messages. In this paper we present the application of a cognitive-inspired algorithm [2, 3, 4] for revealing the structure of these dynamic social networks (simulated by the HCMM model [5]) using information about physical encounters logged by the users' mobile devices. The main features of our algorithm are: (i) the capacity of detecting social communities induced by physical co-location of users through distributed algorithms, (ii) the capacity to detect users belonging to more communities (thus acting as bridges across them), and (iii) the capacity to detect the time evolution of communities.
Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility

In this article, we account for the presence of heterogeneous leverage effects and the persistence in the volatility of stock index realized volatility. The Heterogeneous Autoregressive (HAR) Realized Volatility (RV) model is extended in order to account for asymmetric responses to negative and positive shocks occurring at distinct frequencies, as well as, for the long range dependence in the heteroscedastic variance of the residuals. Compared with established HAR and Autoregressive Fractionally Integrated Moving Average (ARFIMA) realized volatility models, the proposed model exhibits superior in sample fitting, as well as, out of sample volatility forecasting performance. The latter is further improved when the Realized Power Variation (RPV) is used as a regressor, while we show that our analysis is also robust against microstructure noise.
Brucella Melitensis 16M Regulates the Effect of AIR Domain on Inflammatory Factors, Autophagy, and Apoptosis in Mouse Macrophage through the ROS Signaling Pathway

Brucellosis is a highly contagious zoonosis caused by Brucella. Brucella can invade and persist inside host cells, which results in chronic infection. We constructed AIR interference and overexpression lentiviruses to acquire AIR interference, overexpression, and rescue stable expression cell lines. We also established a Brucella melitensis 16M-infected macrophage model, which was treated with either the vehicle control or NAC (ROS scavenger N-acetylcysteine (NAC) for 0, 3, 6, 12, and 24 h. Confocal laser microscopy, transmission electron microscopy, fluorescence quantitative PCR, flow cytometry, ELISA, and Western blot were used to detect inflammation, cell autophagy and apoptosis-related protein expression levels, ROS levels, and the distribution of mitochondria. It was found that after interference and overexpression of AIR, ROS release was significantly changed, and mitochondria became abnormally aggregated. B. melitensis 16M activated the NLRP3/AIM2 inflammatory complex, and induced RAW264.7 cells to secrete IL-1β and IL-18 through the ROS pathway. B. melitensis 16M also altered autophagy-related gene expression, increased autophagy activity, and induced cell apoptosis through the ROS pathway. The results showed that after B. melitensis 16M infection, ROS induced apoptosis, inflammation, and autophagy while AIR inhibited autophagosome maturation and autophagy initiation. Autophagy negatively regulated the activation of inflammasomes and prevented inflammation from occurring. In addition, mitophagy could promote cell apoptosis.
Constructing States for Reinforcement Learning

POMDPs are the models of choice for reinforcement learning (RL) tasks where the environment cannot be observed directly. In many applications we need to learn the POMDP structure and parameters from experience and this is considered to be a difficult problem. In this paper we address this issue by modeling the hidden environment with a novel class of models that are less expressive, but easier to learn and plan with than POMDPs. We call these models deterministic Markov models (DMMs), which are deterministic-probabilistic finite automata from learning theory, extended with actions to the sequential (rather than i.i.d.) setting. Conceptually, we extend the Utile Suffix Memory method of McCallum to handle long term memory. We describe DMMs, give Bayesian algorithms for learning and planning with them and also present experimental results for some standard POMDP tasks and tasks to illustrate its efficacy.
The radius and effective temperature of the binary Ap star β CrB from CHARA/FLUOR and VLT/NACO observations

Context. The prospects for using the asteroseismology of rapidly oscillating Ap (roAp) stars are hampered by the large uncertainty in fundamental stellar parameters. Results in the literature for the effective temperature (Teff) often span a range of 1000 K. Aims. Our goal is to reduce systematic errors and improve the Teff calibration of Ap stars based on new interferometric measurements. Methods. We obtained long-baseline interferometric observations of β CrB using the CHARA/FLUOR instrument. To disentangle the flux contributions of the two components of this binary star, we obtained VLT/NACO adaptive optics images. Results. We determined limb-darkened angular diameters of 0.699 ± 0.017 mas for β CrB A (from interferometry) and 0.415 ± 0.017 mas for β CrB B (from surface brightness-colour relations), corresponding to radii of 2.63 ± 0.09 R� (3.4% uncertainty) and 1.56 ± 0.07 R� (4.5%). The combined bolometric flux of the A+B components was determined from satellite UV data, spectrophotometry in the visible, and broadband data in the infrared. The flux from the B component constitutes 16 ± 4% of the total flux and was determined by fitting an ATLAS9 model atmosphere to the broad-band NACO J and K magnitudes. By combining the flux of the A component with its measured angular diameter, we determined the effective temperature Teff (A) = 7980 ± 180 K (2.3%). Conclusions. Our new interferometric and imaging data enable nearly model-independent determination of the effective temperature of β CrB A. Including our recent study of α Cir, we now have direct Teff measurements of two of the brightest roAp stars, providing a strong benchmark for improved calibration of the Teff scale for Ap stars. This will support the use of potentially strong constraints imposed by asteroseismic studies of roAp stars.
Using Acoustic Signal and Image to Achieve Accurate Indoor Localization

Location information plays a key role in pervasive computing and application, especially indoor location-based service, even though a mass of systems have been proposed, an accurate and practical indoor localization system remains unsettled. To tackle this issue, in this paper, we present a new localization scheme, SITE, combining acoustic Signals and Images to achieve accurate and robust indoor locaTion sErvice. Relying on a pre-deployed platform of acoustic sources with different frequencies, using proactively generated Doppler effect signals, SITE could track relative directions between the phone and the sources. Given m (m≥5) relative directions, SITE can use the angle differences to compute a set of locations corresponding to different subsets of sources. Then, based on a key observation—while the simultaneously estimated locations using different sets of acoustic anchors are within a small circle, the results converge to a point near the true location—SITE proposes a decision scheme that confirms whether these locations satisfy the demand of localization accuracy and can be used to search the user’s location. If not, SITE utilizes VSFM(Visual Structure from Motion) technique to achieve a set of relative locations using some images captured by the phone’s camera. By exploiting the synergy between the set of relative locations and the set of initial locations computed by relative directions, an optimal transformation relationship is obtained and applied to refine the initial calculated results. The refined result will be regarded as the user’s location. In the evaluation, we implemented a prototype and deployed a real platform of acoustic sources in different scenarios. Experimental results show that SITE has excellent performance of localization accuracy, robustness and feasibility in practical application.
A Study of Enhanced Robot Autonomy in Telepresence

Many telepresence robots have been developed, but most offer little more than a webcam on wheels, with the user being in control of every function of the robot. In order to enhance the users ability to remotely communicate, navigate and feel present in a remote environment, we argue that a higher degree of autonomy is required. In this paper a telepresence system is presented that assists the user in three core ways: semi-autonomous navigation control, semi- autonomous people tracking and improved situational awareness. The value of enhanced autonomy in telepresence systems is demonstrated by experiments carried out with real users, both with a webcam on wheels and with our proposed telepresence system.
Contact order revisited: Influence of protein size on the folding rate

Guided by the recent success of empirical model predicting the folding rates of small two‐state folding proteins from the relative contact order (CO) of their native structures, by a theoretical model of protein folding that predicts that logarithm of the folding rate decreases with the protein chain length L as L2/3, and by the finding that the folding rates of multistate folding proteins strongly correlate with their sizes and have very bad correlation with CO, we reexamined the dependence of folding rate on CO and L in attempt to find a structural parameter that determines folding rates for the totality of proteins. We show that the Abs_CO = CO × L, is able to predict rather accurately folding rates for both two‐state and multistate folding proteins, as well as short peptides, and that this Abs_CO scales with the protein chain length as L0.70 ± 0.07 for the totality of studied single‐domain proteins and peptides.
Processing and Interpreting Water bodies and Road Networks from Satellite Images and storing them as QR Codes

— Study on remote sensing images is becoming an important target for geological survey activities, mineral exploration, industrial investment, etc. A detailed study and observation about an image is essential to classify every object in the image. This work aims to classify water bodies and road networks from satellite images. Satellite image analysis provides a statistically superior method of sampling that is not possible via conventional ground "grab sampling" methods. Satellite imaging can analyze the entire body of water and identify the areas that need treatment or can be used for planning purposes. However, heavy cloud obstruction or vegetation coverage will impact the ability to view and analyze water or land. Also, remotely sensed images may be severely affected due to different kinds of noise. In order to overcome this, initially, the image is checked for noise and is removed, if necessary using an iterative filtering algorithm for impulse noise removal. The denoising is followed by segmentation after which feature extraction or classification can be performed to identify the objects. After classification, the information is to be stored for future reference. The last step is to store back the information in the form of a QR code. Previously, barcodes were used for storing such information. However with the advent of smartphone technology, people are always connected to the internet and the new technology of QR codes is more suitable for this purpose. The information can be used for various purposes like geographical surveys, mapping of regions and can also be used to find out the author or the person who has processed the image for any future clarifications.
A Clustering Algorithm of Trajectories for Behaviour Understanding Based on String Kernels

This work aims to identify abnormal behaviors from the analysis of humans or vehicles' trajectories. A set of normal trajectories' prototypes is extracted by means of a novel unsupervised learning technique: the scene is adaptively partitioned into zones by using the distribution of the training set and each trajectory is represented as a sequence of symbols by taking into account positional information (the zones crossed in the scene), speed and shape. The main novelties of this work are the following: first, the similarity between trajectories is evaluated by means of a kernel-based approach. Furthermore, we define a novel and efficient kernel-based clustering algorithm, aimed at obtaining groups of normal trajectories. The proposed approach has been compared with state-of-the-art methods and it clearly outperforms all the other considered techniques.
Generating Data-driven Hints for Open-ended Programming

Intelligent Tutoring Systems (ITSs) have shown success in the domain of programming, in part by providing customized hints and feedback to students. However, many popular novice programming environments still lack these intelligent features. This is due in part to their use of open-ended programming assignments, which are difficult to support with existing hint generation techniques. In this paper, we present a new data-driven algorithm, based on the Hint Factory, to generate hints for these open-ended assignments. We evaluate our algorithm on historical student data and show that it can provide hints that successfully lead students to solutions from any state, help students achieve assignment objectives, and align with the student’s future solution.
Monotonicity in bargaining networks

We study bargaining networks, discussed in a recent paper of Kleinberg and Tardos [KT08], from the perspective of cooperative game theory. In particular we examine three solution concepts, the nucleolus, the core center and the core median. All solution concepts define unique solutions, so they provide testable predictions. We define a new monotonicity property that is a natural axiom of any bargaining game solution, and we prove that all three of them satisfy this monotonicity property. This is actually in contrast to the conventional wisdom for general cooperative games that monotonicity and the core condition (which is a basic property that all three of them satisfy) are incompatible with each other. Our proofs are based on a primal-dual argument (for the nucleolus) and on the FKG inequality (for the core center and the core median). We further observe some qualitative differences between the solution concepts. In particular, there are cases where a strict version of our monotonicity property is a natural axiom, but only the core center and the core median satisfy it. On the other hand, the nucleolus is easy to compute, whereas computing the core center or the core median is #P-hard (yet it can be approximated in polynomial time).
A vision-based head tracker for fish tank virtual reality-VR without head gear

A practical and robust head-position tracking method using computer vision is presented. By combining two simple image processing techniques, this tracker can, report the position of the user's head in real time. Whole image processing is performed by software running on normal mid-range workstations. This tracker can support desk top virtual reality (also referred to as "fish tank VR"), thereby enabling a user to use a wide range of 3D systems Without having to the on any equipment. An experiment conducted by the author suggests this tracker can improve the human's ability in understanding complex 3D structures presented on the display.
Cleaning trajectory data of RFID-monitored objects through conditioning under integrity constraints

A probabilistic framework is introduced for reducing the inherent uncertainty of trajectory data collected for RFID-monitored objects. The framework represents the position of an object at each instant as a random variable over the set of possible locatio ns. The probability density function of this random variable is ini tialized according to an a-priori probability distribution, and the n revised by conditioning it w.r. t. the event that integrity constrai nts are satisfied. In particular, integrity constraints implied by the structure of the map of locations and the motility characteristics (su ch as the maximum speed) of the monitored objects are exploited (namely, direct unreachability, latency and minimum traveling timeconstraints). The efficiency and effectiveness of the proposed approach ar e assessed experimentally on synthetic data.
Causal reasoning in graphical models

This paper presents the problem of the identification of the causal relations that agents, in front of a sequence of reported events, may attribute on the basis of their beliefs on the course of things and available pieces of information. In particular, we focus on graphical models exploiting the idea of "intervention", initially proposed in the probability framework by Pearl, and developed in the more qualitative setting of the theory of possibilities within the french national project called MICRAC. We show that interventions, which are very useful for representing causal relations between events, can be naturally viewed as a belief change process. This paper also provides an overview of main compact representation formats, and their associated inference tools, that exist in a possibility theory framework.
Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package

For statistical analysis of functional magnetic resonance imaging (fMRI) data sets, we propose a data-driven approach based on independent component analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.
Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM) classifier. Specifically, Gabor filtering and multihypothesis (MH) prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM) and MH-prediction-based SVM in challenging small training sample size conditions.