Miracle-dz/UniFAR-FTUs huggingface dataset:
Authentication of Moving Top-k Spatial Keyword Queries

A moving top-k spatial keyword (MkSK) query, which takes into account a continuously moving query location, enables a mobile client to be continuously aware of the top-k spatial web objects that best match a query with respect to location and text relevance. The increasing mobile use of the web and the proliferation of geo-positioning render it of interest to consider a scenario where spatial keyword search is outsourced to a separate service provider capable at handling the voluminous spatial web objects available from various sources. A key challenge is that the service provider may return inaccurate or incorrect query results (intentionally or not), e.g., due to cost considerations or invasion of hackers. Therefore, it is attractive to be able to authenticate the query results at the client side. Existing authentication techniques are either inefficient or inapplicable for the kind of query we consider. We propose new authentication data structures, the MIR-tree and MIR*-tree, that enable the authentication of MkSK queries at low computation and communication costs. We design a verification object for authenticating MkSK queries, and we provide algorithms for constructing verification objects and using these for verifying query results. A thorough experimental study on real data shows that the proposed techniques are capable of outperforming two baseline algorithms by orders of magnitude.
SRCDD: Secure Route Construction and Data Dissemination Protocol for Wireless Sensor Networks

As a consequence of widespread usage, wireless sensor networks has become a hot topic, and routing issues in wireless sensor networks have attracted many researchers. On of the most importing things in routing protocols is security issue. As a nature of wireless sensor networks, they are prone to many malicious actions. So the routing protocol should be secure enough to guarantee the data security. In this paper, we present a routing protocol, which is secure in both cases of route construction, and data dissemination. We have simulated this protocol using NS2.
Long tails in deep columns of natural and anthropogenic tropospheric tracers

Simple prototypes for forced advection‐diffusion problems are known to produce passive tracer distributions that exhibit approximately exponential or stretched exponential tails. Having previously found an approximately exponential tail for the column integrated water vapor (CWV) distribution under high precipitation conditions, we conjectured that if such prototypes are relevant to more complex tropospheric tracer problems, we should find such tails for a wide set of tracers. Here it is shown that such tails are indeed ubiquitous in observed, model, and reanalysis data sets for a variety of tracers, either column integrated or averaged through a deep layer, including CO and CO2. The long tails in CWV are associated with vertical transport and can occur independent of a local precipitation sink. These non‐Gaussian distributions can have consequences for source attribution studies of anthropogenic tracers, and for mechanisms of precipitation extremes; the properties of the tails may help constrain model tracer simulations.
A Time-Enhanced Collaborative Filtering Approach

Collaborative filtering can predict an active user's interests for unrated items based on his observed ratings, and the issue of concept drift exists in most of recommender systems. Aiming at the issue of concept drift, a time-enhanced collaborative filtering approach is proposed in this work, in which a time weight is introduced into the framework of collaborative filtering. As the experimental results show, the proposed approach improves the recommendation accuracy in contrast with the basic collaborative filtering.
Linear-time disk-based implicit graph search

Many search algorithms are limited by the amount of memory available. Magnetic disk storage is over two orders of magnitude cheaper than semiconductor memory, and individual disks can hold up to a terabyte. We augment memory with magnetic disks to perform brute-force and heuristic searches that are orders of magnitude larger than any previous such searches. The main difficulty is detecting duplicate nodes, which is normally done with a hash table. Due to long disk latencies, however, randomly accessed hash tables are infeasible on disk, and are replaced by a mechanism we call delayed duplicate detection. In contrast to previous work, we perform delayed duplicate detection without sorting, which runs in time linear in the number of nodes in practice. Using this technique, we performed the first complete breadth-first searches of the 2 × 7, 3 × 5, 4 &times 4, and 2 ×× 8 sliding-tile Puzzles, verifying the radius of the 4 × 4 puzzle and determining the radius of the others. We also performed the first complete breadth-first searches of the four-peg Towers of Hanoi problem with up to 22 discs, discovering a surprising anomaly regarding the radii of these problems. In addition, we performed the first heuristic searches of the four-peg Towers of Hanoi problem with up to 31 discs, verifying a conjectured optimal solution length to these problems. We also performed partial breadth-first searches of Rubik's Cube to depth ten in the face-turn metric, and depth eleven in the quarter-turn metric, confirming previous results.
Association of Mobile User in Wi-Fi networks- a review

In last decade, number of internet users are increasing so as Wi-Fi network usage is also increasing. Users are using Wi-Fi network for the internet facility. Wi-Fi network is a collection of Mobile Users [MUs] and Access Points [APs]. Because of unavailability of smart association techniques, sometimes mobile users are associated to single access point beyond the capacity of the access point, at that time load on access point is increased. While other access points have less mobile users. So it is necessary to balance the load. To balance the load in Wi-Fi network, various techniques like: received signal strength indicator, least load first, fair bandwidth allocation, smart association control, cell breathing, fuzzy, load balancing with fast handoff scheme and distributed association. These techniques working differently and use various criteria for load balancing in Wi-Fi network. Study all the technique and comparative analysis is done in this paper.
Query-level stability and generalization in learning to rank

This paper is concerned with the generalization ability of learning to rank algorithms for information retrieval (IR). We point out that the key for addressing the learning problem is to look at it from the viewpoint of query. We define a number of new concepts, including query-level loss, query-level risk, and query-level stability. We then analyze the generalization ability of learning to rank algorithms by giving query-level generalization bounds to them using query-level stability as a tool. Such an analysis is very helpful for us to derive more advanced algorithms for IR. We apply the proposed theory to the existing algorithms of Ranking SVM and IRSVM. Experimental results on the two algorithms verify the correctness of the theoretical analysis.
Prediction of Nonalcoholic Fatty Liver Disease Via a Novel Panel of Serum Adipokines

Abstract Considering limitations of liver biopsy for diagnosis of nonalcoholic liver disease (NAFLD), biomarkers’ panels were proposed. The aims of this study were to establish models based on serum adipokines for discriminating NAFLD from healthy individuals and nonalcoholic steatohepatitis (NASH) from simple steatosis. This case-control study was conducted in patients with persistent elevated serum aminotransferase levels and fatty liver on ultrasound. Individuals with evidence of alcohol consumption, hepatotoxic medication, viral hepatitis, and known liver disease were excluded. Liver biopsy was performed in the remaining patients to distinguish NAFLD/NASH. Histologic findings were interpreted using “nonalcoholic fatty liver activity score. ” Control group consisted of healthy volunteers with normal physical examination, liver function tests, and liver ultrasound. Binary logistic regression analysis was applied to ascertain the effects of independent variables on the likelihood that participants have NAFLD/NASH. Decreased serum adiponectin and elevated serum visfatin, IL-6, TNF-a were associated with an increased likelihood of exhibiting NAFLD. NAFLD discriminant score was developed as the following: [(−0.298 × adiponectin) + (0.022 × TNF-a) + (1.021 × Log visfatin) + (0.709 × Log IL-6) + 1.154]. In NAFLD discriminant score, 86.4% of original grouped cases were correctly classified. Discriminant score threshold value of (−0.29) yielded a sensitivity and specificity of 91% and 83% respectively, for discriminating NAFLD from healthy controls. Decreased serum adiponectin and elevated serum visfatin, IL-8, TNF-a were correlated with an increased probability of NASH. NASH discriminant score was proposed as the following: [(−0.091 × adiponectin) + (0.044 × TNF-a) + (1.017 × Log visfatin) + (0.028 × Log IL-8) − 1.787] In NASH model, 84% of original cases were correctly classified. Discriminant score threshold value of (−0.22) yielded a sensitivity and specificity of 90% and 66% respectively, for separating NASH from simple steatosis. New discriminant scores were introduced for differentiating NAFLD/NASH patients with a high accuracy. If verified by future studies, application of suggested models for screening of NAFLD/NASH seems reasonable.
A Layout-Analysis Based System for Document Image Retrieval ! ! !

Document Image Retrieval! G. Pirlo , M. Chimienti, M. Dassisti, D. Impedovo, A. Galiano ! Abstract. This paper presents new system for document image retrieval, based on layout-analysis. The system, that is well suited for commercial form retrieval, uses Radon Transform for layout description and Dynamic Time Warping for document image matching. The experimental results, that were conducted using real and simulated data sets, demonstrate the proposed approach is effective and robust with respect to different classes of commercial forms and shifted/rotated document images.
Physical Aware Frequency Selection for Dynamic Thermal Management in Multi-Core Systems

In order to maintain performance per watt in microprocessors, there is a shift towards the chip level multiprocessing paradigm. Microprocessor manufacturers are experimenting with tens of cores, forecasting the arrival of hundreds of cores per single processor die in the near future. With such large-scale integration and increasing power densities, thermal management continues to be a significant design effort to maintain performance and reliability in modern process technologies. In this paper, we present two mechanisms to perform frequency scaling as part of dynamic frequency and voltage scaling (DVFS) to assist dynamic thermal management (DTM). Our frequency selection algorithms incorporate the physical interaction of the cores on a large-scale system onto the emergency intervention mechanisms for temperature reduction of the hotspot, while aiming to minimize the performance impact of frequency scaling on the core that is in thermal emergency. Our results show that our algorithm consistently succeeds in maximizing the operating frequency of the most critical core while successfully relieving the thermal emergency of the core. A comparison of our two alternative techniques reveals that our physical aware criticality-based algorithm results in 11.7% faster clock frequencies compared to our aggressive scaling algorithm. We also show that our technique is extremely fast and is suited for real time thermal management
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to learn a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further delved into the adversarial training to strengthen the correlation between the inputs and corresponding outputs. Our approach is generative to learn hash functions, such that the learned hash codes can maximally correlate each input–output correspondence and also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method outperforms the state of the arts.
Detecting audio events for semantic video search

This paper describes our work on audio event detection, one of our tasks in the European project VIDIVIDEO. Preliminary experiments with a small corpus of sound effects have shown the potential of this type of corpus for training purposes. This paper describes our experiments with SVM classifiers, and different features, using a 290-hour corpus of sound effects, which allowed us to build detectors for almost 50 semantic concepts. Although the performance of these detectors on the development set is quite good (achieving an average F-measure of 0.87), preliminary experiments on documentaries and films showed that the task is much harder in real-life videos, which so often include overlapping audio events. Index Terms: event detection, audio segmentation
Weight Loss in Response to Food Deprivation Predicts the Extent of Diet-Induced Obesity in C57BL/6J Mice

Inbred C57BL/6J mice have been used to study diet-induced obesity and the detrimental physiological effects associated with it. Little is understood about predictive factors that predispose an animal to weight gain. To address this, mice were fed a high fat diet, control diet or normal chow diet. Several measurements including pre-diet serum hormone levels and pre-diet body weight were analyzed, but these had limited predictive value regarding weight gain. However, baseline measurements of weight loss in response to food deprivation showed a strong negative correlation with high fat diet-induced weight gain. These data suggest that fasting-induced weight loss in adolescent mice is a useful predictor of diet-induced weight gain.
Les Houches lectures on matrix models and topological strings

In these lecture notes for the Les Houches School on Applications of Random Matrices in Physics we give an introduction to the connections between matrix models and topological strings. We first review some basic results of matrix model technology and then we focus on type B topological strings. We present the main results of Dijkgraaf and Vafa describing the spacetime string dynamics on certain Calabi-Yau backgrounds in terms of matrix models, and we emphasize the connection to geometric transitions and to large N gauge/string duality. We also use matrix model technology to analyze large N Chern-Simons theory and the Gopakumar-Vafa transition.
A general model for nested transactional workflows

This paper applies concepts from transaction processing to workkows, thus enabling workkows to exhibit relaxed transactional behaviour. A general model for transactional workkows is presented. We deene correctness of transactional workkows in terms of the model, and use the model to nd schedules such that the execution of a transactional workkow is guaranteed to terminate in one of its acceptable termination (commit or abort) states. We allow a transactional workkow to consist of a number of tasks composed by the constructs ordering, contingency, alternative, conditional and iteration. In addition, we show how the model can be used to support proper nesting of transactional workkows to reduce the diiculty of scheduling algorithms and to provide for structured and modular TWF speciication.
An Efficient Identity-Based Authenticated Email Protocol with Perfect Forward Secrecy

Nowadays, email has become the most widely communication way in the daily life. By using identity-based encryption scheme, we propose a high efficient identity-based authenticated email protocol, which provide perfect forward secrecy. In contrast to previous email protocols, our scheme does not use PKI and so it avoids complex certificate and key management problems. And we prove that our scheme satisfies the security property of confidentiality assuming that the Computational Diffie-Hellman problem is hard. In addition, our scheme has low computation cost and little bandwidth demand which is suitable for mobile communication environment.
Alignment of Monophonic and Polyphonic Music to a Score

Music alignment is the association of events in a score with points in the time axis of an audio signal. The signal is thus segmented according to the events in the score. We propose a new methodology for automatic alignment based on dynamic time warping, where the spectr al peak structure is used to compute the local distance, enhanced by a model of attacks and of silence. The methodology can cope with performances consider ed difficult to align, like polyphonic music, trills, fast sequences, or multi-instrument music. An optimisation of the representation of the alignment path makes the method applicable to long sound files, so that unit databases can be fully automatically segmented and labeled. On 708 sequences of synthesised music, we achieved an averag e offset of 25 ms and an error rate of 2.5%.
Lymphoma-targeted treatment using a folic acid-decorated vincristine-loaded drug delivery system

Purpose B-cell lymphoma is the most frequently diagnosed lymphoid tumor. Folic acid (FA)-decorated systems were found to be preferentially internalized on the B-cell lymphoma cell line which is reported to express the folate receptor. This study was designed to develop an FA-decorated vincristine (VCR)-loaded system for targeted lymphoma treatment. Methods FA-decorated lipid was synthesized. VCR-loaded lipid-polymer hybrid nanoparticles (LPNs) were fabricated. In vitro cell lines and an in vivo lymphoma animal model was used to evaluate the anti B-cell lymphoma effect. Results FA-decorated, VCR-loaded LPNs (FA-VCR/LPNs) have shown a targeted effect in delivery to B-cell lymphoma cells. FA-VCR/LPNs also showed the highest anti-tumor effect in murine-bearing lymphoma xenografts. Conclusion FA-VCR/LPNs can achieve targeted delivery of VCR, bring about an outstanding therapeutic effect to treat lymphoma, and also reduce the systemic toxicity. FA-VCR/LPNs could be an excellent system for lymphoma therapy.
A Robust Multi-dimensional Poverty Profile for Uganda

Abstract We compute a Multi-dimensional Poverty Index (MPI) for Uganda following the specification by Alkire and Santos and the general approach of Alkire and Forster. Using household survey data we show how the incidence of multi-dimensional poverty has fallen in recent years and we use the decomposability features of the index to explain the reduction in multi-dimensional poverty. The robustness of our results is tested in a stochastic dominance framework and using statistical inference. Notably, we extend the one-dimensional analysis of stochastic dominance to include household size as a second dimension, thus taking into account that MPI indicators are collected at both household and individual levels. Moreover, we extend the standard two-stage application of the MPI to include a third stage, which is important given the high degree of multiple deprivations within the standard of living dimension.
Wireless Generations: Transitions and Multimedia Suitability

Wireless technology is accelerating so rapidly. With the evolution of wireless technologies (GPRS, EDGE, cdma2000, W-CDMA), wireless networks is aiming to alter from voice-centric networks to an efficient and flexible data networks. This development beside the advancements in wireless computing and devices had allowed an increased number of multimedia applications to appear in our era. Many components are coming together to make mobile multimedia a reality. This paper will investigate the road map of mobile data networks, their evolution and capabilities to handle today and future increased-demand for wireless multimedia applications. The focus will aim third generation (3G) wireless networks and the amendment toward fourth generation (4G)
Low power layered space-time channel detector architecture for MIMO systems

This paper presents the low power implementation of a maximum likelihood (ML) based detector used in the receiver part of a multiple input and multiple output (MIMO) systems. Low power is mainly achieved through complexity reduction of the ML detector. In particular, Manhattan metric approach is proposed for removing the need for the use of multipliers in the architecture, leading to significant complexity reduction in the ML detector implementation with only 0.7 dB loss in the bit error rate (BER) performance. Results are presented showing that our ML detector achieves 29% saving in area and 34.4% saving in power consumption compared to conventional implementations
Per-Subchannel Joint Equalizer and Receiver Filter Design in OFDM/OQAM Systems

Orthogonal-frequency-division-multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) based multicarrier system achieves better spectral efficiency in comparison with the widely used multicarrier OFDM system. However, the OFDM/OQAM system suffers from intersubchannel interference and intersymbol interference in frequency-selective channels, which cause poor bit error rate (BER) performance. To overcome these challenging issues, in this paper, we propose a per-subchannel joint equalizer and receiver filter design method based on maximizing signal-to-interference-plus-noise ratio (SINR) of each subchannel. An iterative two-step algorithm is developed to design equalizer and receiver filter jointly. At the first step, by assuming that the receiver filter impulse response is identified, an equalizer is designed for each subchannel by maximizing its SINR. At the second step, by assuming that the channel and equalizer impulse responses are known, the SINR of each subchannel is derived based on the receiver filter impulse response coefficients. In this way, the receiver filter of each subchannel is designed independently by maximizing the SINR of that subchannel. The convergence of the proposed iterative design algorithm is proven and also the designing procedure is extended for multi-input-multi-output channels. Simulation results show that the proposed OFDM/OQAM system, in addition of its superior bandwidth efficiency, significantly achieves better performance in comparison with the OFDM system. Moreover, the convergence speed of the iterative SINR maximization algorithm is evaluated by numerical assessments under different scenarios.
Performance of Mahalanobis Distance in Time Series Classification Using Shapelets

Time series data are sequences of values measured over time. One of the most recent approaches to classification of time series data is to find shapelets within a data set. Time series shapelets are time series subsequences which represent a class. In order to compare two time series sequences, existing work uses Euclidean distance measure. The problem with Euclidean distance is that it requires data to be standardized if scales differ. In this paper, we perform classification of time series data using time series shapelets and used Mahalanobis distance measure. The Mahalanobis distance is a descriptive statistic that provides a relative measure of a data point's distance (residual) from a common point. The Mahalanobis distance is used to identify and gauge similarity of an unknown sample set to a known one. It differs from Euclidean distance in that it takes into account the correlations of the data set and is scale-invariant. We show that use of Mahalanobis distance measure instead of Euclidean distance measure in time series dataset classification using shapelets leads to increase in accuracy.