Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive generic learning method (IGLM): a new approach to interactive short text classification

We propose a method to improve the performance of Random Forests for classifying short texts interactively. In short text classification, the principle of learning algorithms is to build a static model using a training dataset, then to use this model to classify new texts. Many works concentrate on improving the representation of the data as a way to build better models. We intend to tackle the problem in two ways: first by abstracting data to solve the problem of sparseness, and second by taking benefit from already classified data to continuously improve the model. Besides, in order to alleviate the amount of manual annotation, we propose an interactive method in which a manual correct annotation is required only for some misclassified texts, which are then incorporated into the training data to build an updated model. An important challenge is then to determine when to trigger this operation and how to perform the update. Applied on the standard search-snippets dataset, our method allowed a significant improvement.
Symbolic CTL Model Checking for Incomplete Designs by Selecting Property-Specific Subsets of Local Component Assumptions

Black Box symbolic model checking is a method to check whether an incompletely specified circuit, in which some parts of the design have been replaced by ‘Black Boxes’, satisfies a CTL property regardless of the actual replacement of the Black Boxes. One possible application is model checking with abstraction where complex parts of the design (which are not really relevant for the property at hand) are put into Black Boxes in order to speed up the model checking run. However, due to its approximative nature, symbolic model checking for incomplete designs may be unable to provide proofs when the approximations are to coarse. In this paper we show how Black Box model checking can profit from information about the BlackBoxed parts given in form of so-called local component assumptions. The assumptions we use in this context are safety properties arguing about fixed time windows. Furthermore we will present a set of heuristic-based approaches to automatically select a subset of local component assumptions that is sufficient to prove or disprove a given CTL property.
Live VM migration techniques in cloud environment — A survey

Cloud computing is a service where storage and computing resources can be accessed on subscription basis. Cloud computing is powered by the concept of virtualization technology. The virtual machines (VM) are hosted in servers so that user's requests are serviced in an optimal manner. The process of moving a running virtual machine or application between different physical machines without disconnecting the client or application is referred to as Live Migration. System resources memory, storage, process and Network resources like connectivity that are allocated to the virtual machine are transferred from the original host machine to the destination machine. Live Migration is performed for achieving Energy efficiency, Load Balancing and High availability of physical servers in Cloud Data center. This paper presents a detailed survey on Live Migration of Virtual machines in cloud environment.
Online budgeted matching in random input models with applications to Adwords

We study an online assignment problem, motivated by Adwords Allocation, in which queries are to be assigned to bidders with budget constraints. We analyze the performance of the Greedy algorithm (which assigns each query to the highest bidder) in a randomized input model with queries arriving in a random permutation. Our main result is a tight analysis of Greedy in this model showing that it has a competitive ratio of 1 - 1/e for maximizing the value of the assignment. We also consider the more standard i.i. d. model of input, and show that our analysis holds there as well. This is to be contrasted with the worst case analysis of [MSVV05] which shows that Greedy has a ratio of 1/2, and that the optimal algorithm presented there has a ratio of 1 - 1/e. The analysis of Greedy is important in the Adwords setting because it is the natural allocation algorithm for an auction-style process. From a theoretical perspective, our result simplifies and generalizes the classic algorithm of Karp, Vazirani and Vazirani for online bipartite matching. Our results include a new proof to show that the Ranking alforithm of [KVV90] has a ratio of 1 - 1/e in the worst case. It has been recently discovered [KV07] (independent of our results) that one of the crucial lemmas in [KVV90], related to a certain reduction, is incorrect. Our proof is direct, in that it does not go via such a reduction, which also enables us to generalize the analysis to our online assignment problem.
Users versus models: what observation tells us about effectiveness metrics

Retrieval system effectiveness can be measured in two quite different ways: by monitoring the behavior of users and gathering data about the ease and accuracy with which they accomplish certain specified information-seeking tasks; or by using numeric effectiveness metrics to score system runs in reference to a set of relevance judgments. In the second approach, the effectiveness metric is chosen in the belief that user task performance, if it were to be measured by the first approach, should be linked to the score provided by the metric. This work explores that link, by analyzing the assumptions and implications of a number of effectiveness metrics, and exploring how these relate to observable user behaviors. Data recorded as part of a user study included user self-assessment of search task difficulty; gaze position; and click activity. Our results show that user behavior is influenced by a blend of many factors, including the extent to which relevant documents are encountered, the stage of the search process, and task difficulty. These insights can be used to guide development of batch effectiveness metrics.
Classification of ncRNAs using position and size information in deep sequencing data

Motivation: Small non-coding RNAs (ncRNAs) play important roles in various cellular functions in all clades of life. With next-generation sequencing techniques, it has become possible to study ncRNAs in a high-throughput manner and by using specialized algorithms ncRNA classes such as miRNAs can be detected in deep sequencing data. Typically, such methods are targeted to a certain class of ncRNA. Many methods rely on RNA secondary structure prediction, which is not always accurate and not all ncRNA classes are characterized by a common secondary structure. Unbiased classification methods for ncRNAs could be important to improve accuracy and to detect new ncRNA classes in sequencing data. Results: Here, we present a scoring system called ALPS (alignment of pattern matrices score) that only uses primary information from a deep sequencing experiment, i.e. the relative positions and lengths of reads, to classify ncRNAs. ALPS makes no further assumptions, e.g. about common structural properties in the ncRNA class and is nevertheless able to identify ncRNA classes with high accuracy. Since ALPS is not designed to recognize a certain class of ncRNA, it can be used to detect novel ncRNA classes, as long as these unknown ncRNAs have a characteristic pattern of deep sequencing read lengths and positions. We evaluate our scoring system on publicly available deep sequencing data and show that it is able to classify known ncRNAs with high sensitivity and specificity. Availability: Calculated pattern matrices of the datasets hESC and EB are available at the project web site http://www.bio.ifi.lmu.de/ALPS. An implementation of the described method is available upon request from the authors. Contact: florian.erhard@bio.ifi.lmu.de
High amyloid‐β deposition related to depressive symptoms in older individuals with normal cognition: a pilot study

Previous studies have reported depressive symptoms in the preclinical stages of Alzheimer's disease (AD). The objective of this study was to determine whether depressive symptoms are associated with cortical amyloid burden. In order to do this, we measured cortical amyloid via 11C‐labeled Pittsburgh Compound B ([11C]PIB) uptake using positron emission tomography (PET) in cognitively normal subjects.
Research on Early Fault Diagnostic Method of Wind Turbines

Challenging environmental factors combined with high and turbulent winds make serious demands on wind turbines and result in significant component fault rates. In this paper, an early fault diagnostic research is conducted upon wind turbines. Firstly, the SCADA (Supervisory Control and Data Acquisition) system is used to analyze the units’ long-hour operating data, preparing for the further modeling work. Then the MSET (Multivariate State Estimation Technique) is adopted to estimate the temperature of the gear box and to obtain a result of high accuracy; with the Moving Window Calculation (MWC), the residual value between the estimated value and the real value is studied to get the dynamic trend of its average value; according to this trend in training, we define the threshold region of the residual mean value. Considering a man-made deviation in the observation vectors, faults of the gear box are simulated and studied. When the residual mean value curve exceeds the setting thresholds, an alert will be given to remind the operators of hidden problems in the unit. Research shows that this early diagnostic method is quite effective in detecting the abnormal performance of wind turbines in a real-time manner. DOI: http://dx.doi. org/10.11591/telkomnika.v11i5. 2457
Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression

How can we scale-up logistic regression, or L1 regularized loss minimization in general, for Terabyte-scale data which do not fit in the memory? How to design the distributed algorithm efficiently? Although there exist two major algorithms for logistic regression, namely Stochastic Gradient Descent (SGD) and Stochastic Coordinate Descent (SCD), they face limitations in distributed environments. Distributed SGD enables data parallelism (i.e., different machines access different part of the input data), but it does not allow feature parallelism (i.e., different machines compute different subsets of the output), and thus the communication cost is high. On the other hand, Distributed SCD allows feature parallelism, but it does not allow data parallelism and thus is not suitable to work in distributed environments. In this paper we propose DF-DSCD (Data/Feature Distributed Stochastic Coordinate Descent), an efficient distributed algorithm for logistic regression, or L1 regularized loss minimization in general. DF-DSCD allows both data and feature parallelism. The benefits of DF-DSCD are (a) full utilization of the capabilities provided by modern distributing computing platforms like MapReduce to analyze web-scale data, and (b) independence of each machine in updating parameters with little communication cost. We prove the convergence of DF-DSCD both theoretically, and also show empirical evidence that it is scalable, handles very high-dimensional data with up to 29 millions of features, and converges 2.2 times faster than competitors.
The Cognitive and Behavioral Treatment of PTSD, What Is Known and What Is Known to Be Unknown: How Not to Fall Into the Practice Gap

[Clin Psychol Sci Prac 17: 134–143, 2010] Posttraumatic stress disorder is a serious potential consequence of suffering severe trauma. Effective treatments exist, and these include both cognitive and behavioral, mainly exposure-based treatments. How clinical practice can be guided by the research evidence is a difficult question given the research-practice gap, and clinicians have a duty of care to provide the optimum treatment for their individual patients. Such duty includes not only taking into account the evidence from clinical trials but also a recognition that there is considerable potential heterogeneity in trauma patients. Factors that influence which treatment is recommended and where future development lies include the following: any potential advantage one treatment has when there is equivalent efficacy; the persistence of physical injury, pain, and disability; presence of medical illness; persistent or long-term exposure to traumatic stress; comorbidity of psychological disorders; suicide risk and exposure to aversive psychosocial environments. Lastly, the development of positive psychology may offer further treatment options.
Automatic Detection of Multilingual Dictionaries on the Web

This paper presents an approach to query construction to detect multilingual dictionaries for predetermined language combinations on the web, based on the identification of terms which are likely to occur in bilingual dictionaries but not in general web documents. We use eight target languages for our case study, and train our method on pre-identified multilingual dictionaries and theWikipedia dump for each of our languages. © 2014 Association for Computational Linguistics.
Position Estimation Using UWB TDOA Measurements

UWB signal has high ranging precision due to its large bandwidth. In this paper, TDOA ranges measured by UWB signals are used in the position estimation process. The ranges are modeled for both LOS and NLOS environment. A TDOA error-minimizing localization method has been proposed to estimate the locations of blind nodes and its performance is investigated in both LOS and NLOS propagations. Cramer-Rao lower bound (CRLB) on the variance of the blind node positions is also derived for LOS situation. Furthermore, we demonstrate that TDOA error-minimizing method is more robust to range errors than Chan's method
The Successive Pile Sort

The RDI data might lead a policymaker to conclude that feelings are so intense about offshore drilling as to preclude county support for the development of onshore support facilities. The ICMR/IAI data, on the other hand, indicate considerable variation across communities in the level of concern about drilling. Other issues, like overdevelopment and fishing regulations (things people can see right now) are more salient in some communities than in others.
Songrium: a music browsing assistance service based on visualization of massive open collaboration within music content creation community

This paper describes a music browsing assistance service, Songrium (http://songrium.jp), that helps a user enjoy songs while seeing visualization of open collaboration. Songrium focuses on open collaboration for music content creation on the most popular Japanese video-sharing service. Since this open collaboration generates more than half a million video clips with a rich variety of music content, we call it massive open collaboration. To develop a shared understanding of this collaboration we have analyzed, we developed Songrium that visualizes relations among both original songs and derivative works generated from the collaboration. Songrium also features a social annotation framework to verbalize and share various relations among songs, and a flexible ranking mechanism to find interesting songs. After we launched Songrium in August 2012, more than 7,000 users have used our service in which over 98,000 songs and 520,000 derivative works have automatically been registered. We hope Songrium will not only encourage creators to create more derivative works, but also attract consumers to participate in the collaboration as creators.
Beyond Symbolic Processing: Expanding Horizons for Educational Psychology.

Mayer (1992) believes that fractionation in educational research is now giving way to a unified cognitive approach based on subject-matter psychologies, the study of learning strategies, and the learning-as-knowledge construction metaphor. Yet, the paradigm described by Mayer is now being strongly challenged by a number of educational research communities, as different forms of the knowledge construction metaphor vie with one another for dominance. Challengers argue that schools must focus on preparing students for participation in complex multidisciplinary thinking activities that characterize life outside of school and that the dominant U.S. cognitive paradigm has been inadequate for examining those kinds of processes
Levels of transaminases, alkaline phosphatase, and protein in tissues of Clarias gariepienus fingerlings exposed to sublethal concentrations of cadmium chloride

The freshwater fish, Clarias gariepienus fingerlings, were exposed to sublethal concentrations (1.7 and 3.4 mg/L) of cadmium chloride for 12 days. Aspartate aminotransferase (AAT), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and total protein levels were assayed in the gill, brain, and muscle of the fish at regular intervals of 6 and 12 days. The activities of AAT, ALT, and ALP of the treated fishes increased significantly in all the tissues compared with the control fish. Protein level in all the tissues showed a significant decrease in comparison to unexposed controls throughout the experimental periods. These results revealed that cadmium chloride effects the intermediary metabolism of C. gariepienus fingerlings and that the assayed enzymes can work as good biomarkers of contamination. © 2008 Wiley Periodicals, Inc. Environ Toxicol, 2008.
Development and Implementation of Electrical Excitation Techniques for Satellites

This is a multidiscipline paper that describes an extensive analytical and experimental effort to: (1) analyze the exterior SGEMP response of a large, complex, modern satellite, (2) develop a global excitation current-injection test (CIT) technique to evaluate the typical Points of Entry (POE's) of a satellite, and (3) perform experiments using CIT techniques on STARSAT (SGEMP Test, Analysis and Research Satellite.) This test technique was developed to meet the technology need for a method of performing SGEMP coupling tests on typical, controlled penetrations resulting from satellite design constraints realistic, full-scale hardware.
TESTING FOR UNEQUAL RATES OF MORPHOLOGICAL DIVERSIFICATION IN THE ABSENCE OF A DETAILED PHYLOGENY: A CASE STUDY FROM CHARACIFORM FISHES

Abstract This study develops the random phylogenies rate test (RAPRATE), a likelihood method that simulates morphological evolution along randomly generated phylogenies, and uses it to determine whether a considerable difference in morphological diversity between two sister clades of South American fishes should be taken as evidence of differing rates of morphological change or lineage turnover. Despite identical ages of origin, similar species richness, and sympatric geographic distributions, the morphological and ecological diversity of the superfamily Anostomoidea exceeds that of the Curimatoidea. The test shows with 90% confidence (using variance among species as the measure of morphological diversity) or 99% confidence (using volume of occupied morphospace) that the rate of morphological change per unit time in the Anostomoidea likely exceeded that of the Curimatoidea. Variation in the rate of lineage turnover (speciation and extinction rates) is not found to affect greatly the morphological diversity of simulated clades and is not a likely explanation of the observed difference in morphological diversity in this case study. Though a 17% or greater delay in the onset of diversification in the Curimatoidea remains a possible alternative explanation of unequal morphological diversification, further simulations suggest that two clades drawn from the possible treespace of the Anostomoidea and Curimatoidea will rarely differ so greatly in the onset of diversification. Several uniquely derived morphological and ecological features of the Anostomoidea and Curimatoidea may have accelerated or decelerated their rate of morphological change, including a marked lengthening of the quadrate that may have relaxed structural constraints on the evolution of the anostomoid jaw.
Gauge-independent approach to resonant transition amplitudes.

We present a new gauge-independent approach to resonant transition amplitudes with nonconserved external currents, based on the pinch technique method. In the context of 2{r_arrow}2 and 2{r_arrow}3 scattering processes we show explicitly that the analytic results derived respect U(1){sub e}{sub m} gauge symmetry and do not depend on the choice of the SU(2){sub {ital L}} gauge fixing. Our analytic approach treats, on equal footing, fermionic as well as bosonic contributions to the resummed gauge boson propagators, does not contain any residual spacelike threshold terms, shows the correct high-energy unitarity behavior, admits renormalization, and satisfies a number of other required properties, including the optical theorem. Even though our analysis has mainly focused on the standard model gauge bosons, our method can easily be extended to the top quark, and be directly applied to the study of unstable particles present in renormalizable models of new physics. {copyright} {ital 1996 The American Physical Society. }
Paradigm shifts in solar dynamo modeling

Abstract Selected topics in solar dynamo theory are being highlighted. The possible relevance of the near-surface shear layer is discussed. The role of turbulent downward pumping is mentioned in connection with earlier concerns that a dynamo-generated magnetic field would be rapidly lost from the convection zone by magnetic buoyancy. It is argued that shear-mediated small-scale magnetic helicity fluxes are responsible for the success of some of the recent large-scale dynamo simulations. These fluxes help in disposing of excess small-scale magnetic helicity. This small-scale magnetic helicity, in turn, is generated in response to the production of an overall tilt in each Parker loop. Some preliminary calculations of this helicity flux are presented for a system with uniform shear. In the Sun the effects of magnetic helicity fluxes may be seen in coronal mass ejections shedding large amounts of magnetic helicity.
A DHT-Based Discovery Service for the Internet of Things

Current trends towards the Future Internet are envisaging the conception of novel services endowed with context-aware and autonomic capabilities to improve end users’ quality of life. The Internet of Things paradigm is expected to contribute towards this ambitious vision by proposing models and mechanisms enabling the creation of networks of “smart things” on a large scale. It is widely recognized that efficient mechanisms for discovering available resources and capabilities are required to realize such vision. The contribution of this work consists in a novel discovery service for the Internet of Things. The proposed solution adopts a peer-to-peer approach for guaranteeing scalability, robustness, and easy maintenance of the overall system. While most existing peer-to-peer discovery services proposed for the IoT support solely exact match queries on a single attribute (i.e., the object identifier), our solution can handle multiattribute and range queries. We defined a layered approach by distinguishing three main aspects: multiattribute indexing, range query support, peer-to-peer routing. We chose to adopt an over-DHT indexing scheme to guarantee ease of design and implementation principles. We report on the implementation of a Proof of Concept in a dangerous goods monitoring scenario, and, finally, we discuss test results for structural properties and query performance evaluation.