Miracle-dz/UniFAR-FTUs huggingface dataset:
FRACTAL DIMENSION AS A DATA DIMENSIONALITY REDUCTION METHOD FOR ANOMALY DETECTION IN TIME SERIES

Many researches show that time series data is a kind of data which has biggest volume and growth compared to the others kind of data. Parallel with its huge volume and rapid growth, it is always needed new method, technique or approach to explore knowledge contained in time series data. One of many goals in data mining of time series is anomaly detection. By definition fractal is an object that has such self similarities in certain degree. This paper presents the results study of HFD (Higuchi Fractal Dimension) approach for clustering to detect the existence of an anomaly or deviation in time series data. This proposed method is applied to PT.PGN daily stock trade year 2004 to 2012 as test data. The results show that for value of discrete interval k = 5, 10, 15 their HFD tend to diverge and there are tend to converge to 2 for the greater value of k. Based on HFD time series clustering results, this approach can be used to divide the normal data and the other data that contain certain anomaly. In this study is also shown that this approach provides a better result compared to adjustment method which fill unbalance time series data with a zero value
Salient Motion Detection in Compressed Domain

We propose a novel motion saliency framework using DCT coefficients and motion vectors as features from MPEG-2 videos. The DCT coefficients of the luma and chroma components are used to calculate the spatial saliency of a frame while the motion vectors are utilized to refine it. Spatio-temporal similarity maps are calculated separately in order to cater to tracking shots. Results show that the proposed motion saliency framework is computationally nearly an 8-fold improvement over the best performing state-of-the-art method while nearly equalling its performance in identifying salient regions.
Unsupervised neural network based feature extraction using weak top-down constraints

Deep neural networks (DNNs) have become a standard component in supervised ASR, used in both data-driven feature extraction and acoustic modelling. Supervision is typically obtained from a forced alignment that provides phone class targets, requiring transcriptions and pronunciations. We propose a novel unsupervised DNN-based feature extractor that can be trained without these resources in zero-resource settings. Using unsupervised term discovery, we find pairs of isolated word examples of the same unknown type; these provide weak top-down supervision. For each pair, dynamic programming is used to align the feature frames of the two words. Matching frames are presented as input-output pairs to a deep autoencoder (AE) neural network. Using this AE as feature extractor in a word discrimination task, we achieve 64% relative improvement over a previous state-of-the-art system, 57% improvement relative to a bottom-up trained deep AE, and come to within 23% of a supervised system.
Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction

We show how to use large biomedical databases in order to obtain a gold standard for training a machine learning system over a corpus of biomedical text. As an example we use the Comparative Toxicogenomics Database (CTD) and describe by means of a short case study how the obtained data can be applied. We explain how we exploit the structure of the database for compiling training material and a testset. Using a Naive Bayes document classification approach based on words, stem bigrams and MeSH descriptors we achieve a macro-average F-score of 61% on a subset of 8 action terms. This outperforms a baseline system based on a lookup of stemmed keywords by more than 20%. Furthermore, we present directions of future work, taking the described system as a vantage point. Future work will be aiming towards a weakly supervised system capable of discovering complete biomedical interactions and events.
Purification and Characterization of Serine Proteinase from a Halophilic Bacterium , Filobacillus sp . RF 2-5

In order to find a unique proteinase, proteinaseproducing bacteria were screened from fish sauce in Thailand. An isolated moderately halophilic bacterium was classified and named Filobacillus sp. RF2-5. The molecular weight of the purified enzyme was estimated to be 49 kDa. The enzyme showed the highest activity at 60 C and pH 10–11 under 10% NaCl, and was highly stable in the presence of about 25% NaCl. The activity was strongly inhibited by phenylmethane sulfonyl fluoride (PMSF), chymostatin, and -microbial alkaline proteinase inhibitor (MAPI). Proteinase activity was activated about 2-fold and 2.5-fold by the addition of 5% and 15–25% NaCl respectively using Suc–Ala–Ala– Phe–pNA as a substrate. The N-terminal 15 amino acid sequence of the purified enzyme showed about 67% identity to that of serine proteinase from Bacillus subtilis 168 and Bacillus subtilis (natto). The proteinase was found to prefer Phe, Met, and Thr at the P1 position, and Ile at the P2 position of peptide substrates, respectively. This is the first serine proteinase with a moderately thermophilic, NaCl-stable, and NaCl-activatable, and that has a unique substrate specificity at the P2 position of substrates from moderately halophilic bacteria, Filobacillus sp.
Road Segmentation in Street View Images Using Texture Information

Road segmentation is a problem encountered fairly frequently, especially in the framework of scene understanding and self-driving cars. On the flip side, there are several Street View databases that offer large amounts of useful data, which are still relatively untapped. In this paper we propose a road segmentation algorithm specifically aimed at segmenting roads from street view images. We use fisher vectors to encode small windows extracted from the main image at multiple scales, then classify these patches and use a voting scheme to get the final segmentation. We optionally utilize a spatial prior and superpixels to improve our segmentation. Our algorithm performs well and outputs a good segmentation for further use in road evaluation. We test our method on the KITTI road dataset and compare it to the state of the art.
Foundations of chaotic mixing

The simplest mixing problem corresponds to the mixing of a fluid with itself; this case provides a foundation on which the subject rests. The objective here is to study mixing independently of the mechanisms used to create the motion and review elements of theory focusing mostly on mathematical foundations and minimal models. The flows under consideration will be of two types: two–dimensional (2D) ‘blinking flows’, or three–dimensional (3D) duct flows. Given that mixing in continuous 3D duct flows depends critically on cross–sectional mixing, and that many microfluidic applications involve continuous flows, we focus on the essential aspects of mixing in 2D flows, as they provide a foundation from which to base our understanding of more complex cases. The baker'stransformation is taken as the centrepiece for describing the dynamical systems framework. In particular, a hierarchy of characterizations of mixing exist, Bernoulli→mixing→ergodic, ordered according to the quality of mixing (the strongest first). Most importantly for the design process, we show how the so–called linked twist maps function as a minimal picture of mixing, provide a mathematical structure for understanding the type of 2D flows that arise in many micromixers already built, and give conditions guaranteeing the best quality mixing. Extensions of these concepts lead to first–principle–based designs without resorting to lengthy computations.
Power loading: Candidate for future WLANs?

IEEE 802.11ac WLANs propose the use of 80 and 160MHz bandwidths. Over such bandwidths the large frequency variability of the channel motivates the dynamic allocation of resources per OFDM subcarrier. In this work we focus on power adaptation (power loading). Besides physical layer performance analysis, we account for protocol overhead and for practical implementation issues in WLANs. In particular, we consider the exploitation of coherence time in 80MHz TGac indoor channels trading-off algorithm accuracy for channel estimation overhead. We further study the trade-off between algorithm performance and computational complexity to conclude that optimal solutions do not necessarily perform better than sub-optimal low-complex ones. We show that pure power loading provides only a modest performance gain compared to static schemes and propose power loading combined with the deactivation of highly attenuated subcarriers as a candidate approach for improving WLAN.
Dust coagulation in star formation with different metallicities

Dust grains coagulate into larger aggregates in dense gas. This changes their size distribution and possibly affects the thermal evolution of star-forming clouds. We here investigate dust coagulation in collapsing pre-stellar cores with different metallicities by considering the thermal motions of grains. We show that coagulation does occur even at low metallicity ∼10 -6 Z ⊙ . However, we also find (i) that the H 2 formation rate on dust grains is reduced only after the majority of H 2 is formed and (ii) that the dust opacity is modified only after the core becomes optically thick. Therefore, we conclude that the effects of dust coagulation can safely be neglected in discussing the temperature evolution of the pre-stellar cores for any metallicity as long as the grain motions are thermal.
Cascade robot force control architecture for autonomous beating heart motion compensation with model predictive control and active observer

Nowadays, robotic-assisted surgery does not allow beating heart surgery with autonomous motion compensation functionalities. This paper tackles this problem, based on a robotic control architecture that relies on force feedback. The algorithm merges two cascade loops. The inner one is based on the Kalman active observer (AOB), performing model-reference adaptive control to impose a well-defined stable plant. The outer one, based on a model predictive control (MPC) approach, generates control references for beating heart motion compensation. Two robots are used in the experiments. A lightweight 4-DoF surgical robot generates desired surgical forces and a 3-DoF robot equipped with an ex vivo heart at the end-effector reproduces realistic heart motion. Additionally, robustness to cardiac stiffness mismatches is analyzed.
Peripheral Arterial Tonometry to Measure the Effects of Vardenafil on Sympathetic Tone in Men with Lifelong Premature Ejaculation

To elucidate whether adrenergic overtone is involved in the pathophysiology of men with lifelong (LL) premature ejaculation (PE), we investigated differences in reactive hyperemia index (RHI) responses by using peripheral arterial tonometry (PAT). 20 men with LL-PE (18–40 years) were enrolled in an 8-week, double-blind, placebo-controlled, crossover study and compared with 10 age-matched controls without LL-PE. Primary endpoints were PAT modifications induced by vardenafil 10 mg on demand. Secondary endpoints were the improvement in intravaginal ejaculatory latency time (IELT) as measured by the stopwatch technique and variations in anxiety scores at Stai-X1 for state-anxiety and Stai-X2 for trait-anxiety. At baseline, men with LL-PE showed higher RHI variation (P < 0.001), Stai-X1 and Stai X2 scores (P < 0.0001, resp.), and prolactin levels (P < 0.05) compared with controls. Vardenafil treatment markedly reduced RHI variation in men with LL-PE (P < 0.01) when compared with placebo. Mean changes in geometric IELT were higher after taking vardenafil (0.6 ± 0.3 versus 4.5 ± 1.1 min, P < 0.01) when compared with placebo. STAI-X1 and STAI-X2 scores fell within the normal range after treatment with vardenafil (P < 0.01). Vardenafil was an effective treatment in men with LL-PE; improvements of IELT may be due to increased NO production which is able to reduce adrenergic overactivity and anxiety levels.
Meta-Learning for Medical Image Classification

Most of the current state-of-the-art methods to classify medical images is to first train a deep model on ImageNet, then transfer all network weights to a new network except for the last softmax layer, and then fine-tune on the target dataset. When the amount of training data in the target dataset is sufficient, this method is able to surpass the level of a trained doctor on several datasets; however, when it is insufficient, which is common in a lot of real medical applications, this method may lead to mediocre results. To address the small dataset problem, we apply a meta-learning method to train, and then fine-tune on the target dataset. We show our results surpass the state-of-the-art method on a popular medical image dataset.
A Cache-Based Flash Translation Layer for TLC-Based Multimedia Storage Devices

Current triple-level cell (TLC)-based solids-tate drives used in multimedia storage devices support multichannel access to increase capacity and throughput. Unfortunately, current state-of-the-art FTL algorithms must employ selective caching for inquiring about the address mapping information, which causes low space utilization, a large flash memory requirement, and performance degradation. In this article, the Cache-based Flash Translation Layer (Cab-FTL) is proposed for TLC-based multimedia storage devices. Cab-FTL enhances the read and write performances by achieving high space utilization while reducing the size of the mapping tables to 1.68% compared to DFTL. Despite a caching of the mapping tables in DRAM, Cab-FTL achieves a fast system boot using its fast wake-up mechanism.
Passivity-Based Pose Synchronization in Three Dimensions

This paper addresses passivity-based pose synchronization in the Special Euclidean group SE(3) . We first develop a passivity-based distributed velocity input law to achieve pose synchronization. We next show that the pose synchronizes exponentially fast under an assumption on the initial states of the bodies, with an exponential convergence rate given by algebraic connectivity of interconnection graphs. We also prove pose synchronization in the presence of communication delays and topology switches. We moreover give further extensions of the present law, where desirable velocities and collision avoidance are taken into account. Finally, the effectiveness of the present inputs is demonstrated through numerical simulations and experiments on a planar (2D) test bed.
A SPARQL Engine for Streaming RDF Data

The basic data format of the semantic Web is RDF. SPARQL, which has been developed by the W3C, is the upcoming standard for RDF query languages. Typical engines for processing SPARQL queries on RDF data first read all RDF data, may build indices of the complete read data and afterwards evaluate SPARQL queries. Such engines cannot operate on streaming RDF data. Streaming query engines operating on streams of data can (a) discard irrelevant input as early as possible, and thus save processing costs and space costs, (b) build indices only on those parts of the data, which are needed for the evaluation of the query, and (c) determine partial results of a query as early as possible, and thus evaluate queries more efficiently. We propose such a streaming SPARQL engine, which is the first streaming SPARQL engine to the best of our knowledge.
Communications in Mathematical Physics Approximation and Equidistribution of Phase Shifts : Spherical Symmetry

Consider a semiclassical Hamiltonian HV,h := h2 + V − E, where h > 0 is a semiclassical parameter, is the positive Laplacian on Rd , V is a smooth, compactly supported central potential function and E > 0 is an energy level. In this setting the scattering matrix Sh(E) is a unitary operator on L2(Sd−1), hence with spectrum lying on the unit circle; moreover, the spectrum is discrete except at 1. We show under certain additional assumptions on the potential that the eigenvalues of Sh(E) can be divided into two classes: a finite number ∼ cd(R √ E/h)d−1, as h → 0, where B(0, R) is the convex hull of the support of the potential, that equidistribute around the unit circle, and the remainder that are all very close to 1. Semiclassically, these are related to the rays that meet the support of, and hence are scattered by, the potential, and those that do not meet the support of the potential, respectively. A similar property is shown for the obstacle problem in the case that the obstacle is the ball of radius R.
Boolean Function Monotonicity Testing Requires (Almost) n 1/2 Non-adaptive Queries

We prove a lower bound of Ω(n1/2-c), for all c> 0, on the query complexity of (two-sided error) non-adaptive algorithms for testing whether an n-variable Boolean function is monotone versus constant-far from monotone. This improves a ~Ω(n1/5) lower bound for the same problem that was obtained in [6], and is very close to the recent upper bound of ~O(n1/2/ε2) by Khot et al. [13].
Transductive Inference for Estimating Values of Functions

We introduce an algorithm for estimating the values of a function at a set of test points xl+1,. , xl+m given a set of training points (x1, Y1),. ,(xl, yl) without estimating (as an intermediate step) the regression function. We demonstrate that this direct (transductive) way for estimating values of the regression (or classification in pattern recognition) can be more accurate than the traditional one based on two steps, first estimating the function and then calculating the values of this function at the points of interest.
Collaborative Software Engineering with a Digital Ecosystem

Adaptation to changing requirements is one of the predominant challenges in distributed development. Software projects exceeding certain scales cannot be addressed by small-sized companies because of the incapability of meeting the financial guarantees and also because of staffing problems. In spite of their advantage of being more flexible, small companies when teaming up are usually overextended due to the overhead of communication and synchronization costs. A need emerges here for an infrastructure endorsing decomposability of software specifications and semi-automatic re-composition of the implemented components to satisfy the original requirements. This infrastructure facilitates a more efficient risk management due to the more finely grained specification. Such an infrastructure is presented here as a result of an ongoing research and implementation activity including real-world testing spanning several European countries and regions
Searching and browsing collections of structural information

This paper proposes a new approach to querying collections of structured textual information such as SGML/XML documents. Knowledge about the structure of documents is an additional resource that should be exploited during retrieval since the semantics of the different textual objects can be used to specify an information need much more precisely. However the traditional probabilistic retrieval model lacks the ability to handle structural information. We define a new retrieval function based on the probabilistic model which overcomes this drawback. The presented query language allows the assignment of structural roles to individual terms. The efficient evaluation of queries in this framework requires appropriate index structures. We design text and structure indexes and show how their information is combined during evaluation. The implementation supports additional functionalities such as a table of contents for browsing. First evaluation results show the feasibility of the approach on collections of unstructured documents.
Awol: Adaptive Write Optimizations in Linux Memory Management and I/o Scheduling in a Shared Read/write Cache

Operating system memory managers fail to consider the population of read versus write pages in the buffer pool and the outstanding I/O requests when writing dirty pages to disk or network file systems. This leads to bursty I/O patterns, which stall processes reading data and reduce the efficiency of disk and file systems. We address these limitations by adaptively allocating memory between write buffering and read caching and by writing dirty pages to disk opportunistically before the operating system submits them for writeback. We implement and evaluate our methods within the Linux operating system and show performance gains of more than 30% for mixed read/write workloads.
Taxonomy-Based Routing Indices for Peer-to-Peer Networks

On the one hand, the lack of structure of Peer-to-Peer (P2P) networks is key to their robustness and accessibility. On the other hand, this same lack of structure creates difficulties in efficiently searching the contents of the network. This search problem must be addressed for P2P networks to grow beyond the world of file sharing. To this end, we present a novel approach for describing the documents accessible through peers as a taxonomy. We propose a scoring function which is used to route queries within the network based on such taxonomical information, as well as the number of results desired by the query. The scoring function aims to minimize the number of network messages required to answer a query. When comparing to a sequential query forwarding algorithm, our simulations have shown that our proposed technique is able to reduce the number of messages generated for a query by a factor of 10. Also, our experiments show that limiting the “time to live” of a query is likely to make queries more expensive.
A heuristic based algorithm for distance-aware virtual machine allocation in cloud

MapReduce-as-a-Service cloud is of great importance because of the data growth and increase in opportunities in big data analytics. MapReduce platforms provided through cloud help the end user by providing ready to use MapReduce clusters. Since the cloud environment is virtualized, allocating Virtual Machines (VMs) efficiently has high relevance. If the VMs allocated for a MapReduce cluster are hosted in distant Physical Machines (PMs), the interaction between VMs causes delays depending upon the distance between the PMs hosting them. In this paper, we propose a heuristic algorithm for VM allocation for providing MapReduce as a cloud service. This algorithm allocates VMs in same or nearby PMs and hence reduces data transfer delay between VMs. Simulation results demonstrate the improvement on execution time of the VM allocation algorithm without compromising the performance of applications running on the allocated VMs.